Identifying Nonstationarity in Turbulence Series

Because of rapid forcing by varying cloud and sky conditions, turbulence time series collected in the atmospheric surface layer over land may often be nonstationary. The meteorological community, however, has no consensus definition of what nonstationarity is and, thus, no consensus method for how to identify it. This study, therefore, adopts definitions for first-order and second-order stationarity taken from the time series analysis literature and implements new analysis techniques and probabilistic tests to quantify first-order and second-order nonstationarity. First-order nonstationarity manifests as a change in the series mean; second-order nonstationarity, as a change in the variance. The analysis identifies nonstationarity in surface-level turbulent temperature and water vapour series collected during two sample days with solar forcing influenced by cirrus and cirrostratus clouds, but that nonstationarity is not as severe as expected despite the rapid thermal forcing by these clouds. On the other hand, even with negligible cloud forcing, both sample days exhibited severe nonstationarity at night.     

Uncertainty, learning and ambiguity in economic models on climate policy: some classical results and new directions

We present how uncertainty and learning are classically studied in economic models. Specifically, we study a standard expected utility model with two sequential decisions, and consider two particular cases of this model to illustrate how uncertainty and learning may affect climate policy. While uncertainty has generally a negative effect on welfare, learning has always a positive, and thus opposite, effect. The effects of both uncertainty and learning on decisions are less clear. Neither uncertainty nor learning can be used as a general argument to increase or reduce emissions today without studying the specific intertemporal costs and benefits. Considering limits in applying the expected utility framework to climate change problems, we then consider a more recent framework with ambiguity-aversion which accounts for situations of imprecise or multiple probability distributions. We discuss both the impact of ambiguity-aversion on decisions and difficulties in applying such a non-expected utility framework to a dynamic context.     

Observing upper troposphere–lower stratosphere climate with radio occultation data from the CHAMP satellite

High quality observations of the atmosphere are particularly required for monitoring global climate change. Radio occultation (RO) data, using Global Navigation Satellite System (GNSS) signals, are well suited for this challenge. The special climate utility of RO data arises from their long-term stability due to their self-calibrated nature. The German research satellite CHAllenging Minisatellite Payload for geoscientific research (CHAMP) continuously records RO profiles since August 2001 providing the first opportunity to create RO based climatologies for a multi-year period of more than 5 years. A period of missing CHAMP data from July 3, 2006 to August 8, 2006 can be bridged with RO data from the GRACE satellite (Gravity Recovery and Climate Experiment). We have built seasonal and zonal mean climatologies of atmospheric (dry) temperature, microwave refractivity, geopotential height and pressure with 10° latitudinal resolution. We show representative results with focus on dry temperatures and compare them with analysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Although we have available only about 150 CHAMP profiles per day (compared to millions of data entering the ECMWF analyses) the overall agreement between 8 and 30 km altitude is in general very good with systematic differences <0.5 K in most parts of the domain. Pronounced systematic differences (exceeding 2 K) in the tropical tropopause region and above Antarctica in southern winter can almost entirely be attributed to errors in the ECMWF analyses. Errors resulting from uneven sampling in space and time are a potential error source for single-satellite climatologies. The average CHAMP sampling error for seasonal zonal means is <0.2 K, higher values occur in restricted regions and time intervals which can be clearly identified by the sampling error estimation approach we introduced (which is based on ECMWF analysis fields). The total error of this new type of temperature climatologies is estimated to be <0.5 K below 30 km. The recently launched Taiwan/U.S. FORMOSAT-3/COSMIC constellation of 6 RO satellites started to provide thousands of RO profiles per day, but already now the single-satellite CHAMP RO climatologies improve upon modern operational climatologies in the upper troposphere–lower stratosphere and can act as absolute reference climatologies for validation of more bias-sensitive climate datasets and models.     

Late Miocene increasing exhumation rates in the eastern part of the Alps – implications from low temperature thermochronology

A new set of apatite fission‐track and apatite (U–Th)/He data reveals a hitherto undated late Miocene exhumation pulse in the eastern part of the Eastern Alps. While distinct parts of the study area, including the Seckauer Tauern, have been at near surface conditions (<100 °C) since the Eocene, the neighbouring Niedere Tauern experienced enhanced cooling and exhumation in the middle Miocene and again at the late Miocene/Pliocene boundary. Middle Miocene exhumation is interpreted as a result of tectonic escape and convergence that operated simultaneously during lateral extrusion of the Eastern Alps. As the higher late Miocene/Pliocene exhumation rates are restricted to a single tectonic block, namely the Niedere Tauern, we infer a tectonic trigger that is probably related to a change in the external stress field that affected the Alps during this time.     

Media attention for climate change around the world: A comparative analysis of newspaper coverage in 27 countries

Climate change is a global phenomenon, and its outcomes affect societies around the world. So far, however, studies on media representations of climate change have mostly concentrated on Western societies. This paper goes beyond this limited geographical scope by presenting a comparative analysis of issue attention in 27 countries. The sample includes, among others, countries that have committed themselves to greenhouse gas emission reductions under the Kyoto Protocol such as Germany as well as countries that are strongly affected by the consequences of climate change like India. In a first step, it describes the development of media attention for climate change in these countries from 1996 to 2010. Second, it compares the amount of media attention and explores whether it corresponds with indicators measuring the relevance of climate change and climate policies for a country. The analyses show that climate change coverage has increased in all countries. Still, overall media attention levels, as well as the extent of growth over time, differ strongly between countries. Media attention is especially high in carbon dependent countries with commitments under the Kyoto Protocol.     

Flow over Hills: A Large-Eddy Simulation of the Bolund Case

Simulation of local atmospheric flows around complex topography is important for several applications in wind energy (short-term wind forecasting and turbine siting and control), local weather prediction in mountainous regions and avalanche risk assessment. However, atmospheric simulation around steep mountain topography remains challenging, and a number of different approaches are used to represent such topography in numerical models. The immersed boundary method (IBM) is particularly well-suited for efficient and numerically stable simulation of flow around steep terrain. It uses a homogenous grid and permits a fast meshing of the topography. Here, we use the IBM in conjunction with a large-eddy simulation (LES) and test it against two unique datasets. In the first comparison, the LES is used to reproduce experimental results from a wind-tunnel study of a smooth three-dimensional hill. In the second comparison, we simulate the wind field around the Bolund Hill, Denmark, and make direct comparisons with field measurements. Both cases show good agreement between the simulation results and the experimental data, with the largest disagreement observed near the surface. The source of error is investigated by performing additional simulations with a variety of spatial resolutions and surface roughness properties.     

Tracking suitable habitat for tree populations under climate change in western North America

An important criticism of bioclimate envelope models is that many wide-ranging species consist of locally adapted populations that may all lag behind their optimal climate habitat under climate change, and thus should be modeled separately. Here, we apply a bioclimate envelope model that tracks habitat of individual populations to estimate adaptational lags for 15 wide-ranging forest tree species in western North America. An ensemble classifier modeling approach (RandomForest) was used to spatially project the climate space of tree populations under observed climate trends (1970s to 2000s) and multi-model projections for the 2020s, 2050s and 2080s. We find that, on average, populations already lag behind their optimal climate niche by approximately 130 km in latitude, or 60 m in elevation. For the 2020s we expect an average lag of approximately 310 km in latitude or 140 m in elevation, with the most pronounced geographic lags in the Rocky Mountains and the boreal forest. We show that our results could in principle be applied to guide assisted migration of planting stock in reforestation programs using a general formula where 100 km north shift is equivalent to approximately 44 m upward shift in elevation. However, additional non-climatic factors should be considered when matching reforestation stock to suitable planting environments.     

Effects of climate change on oceanic fisheries in the tropical Pacific: implications for economic development and food security

The four species of tuna that underpin oceanic fisheries in the tropical Pacific (skipjack, yellowfin, bigeye and albacore tuna) deliver great economic and social benefits to Pacific Island countries and territories (PICTs). Domestic tuna fleets and local fish processing operations contribute 3–20 % to gross domestic product in four PICTs and licence fees from foreign fleets provide an average of 3–40 % of government revenue for seven PICTs. More than 12,000 people are employed in tuna processing facilities and on tuna fishing vessels. Fish is a cornerstone of food security for many PICTs and provides 50–90 % of dietary animal protein in rural areas. Several PICTs have plans to (1) increase the benefits they receive from oceanic fisheries by increasing the amount of tuna processed locally, and (2) allocate more tuna for the food security of their rapidly growing populations. The projected effects of climate change on the distribution of tuna in the tropical Pacific Ocean, due to increases in sea surface temperature, changes in velocity of major currents and decreases in nutrient supply to the photic zone from greater stratification, are likely to affect these plans. PICTs in the east of the region with a high dependence on licence fees for government revenue are expected to receive more revenue as tuna catches increase in their exclusive economic zones. On the other hand, countries in the west may encounter problems securing enough fish for their canneries as tuna are redistributed progressively to the east. Changes in the distribution of tuna will also affect the proportions of national tuna catches required for food security. We present priority adaptations to reduce the threats to oceanic fisheries posed by climate change and to capitalise on opportunities.     

Multi-variable error correction of regional climate models

Climate change impact research needs regional climate scenarios of multiple meteorological variables. Those variables are available from regional climate models (RCMs), but affected by considerable biases. We evaluate the application of an empirical-statistical error correction method, quantile mapping (QM), for a small ensemble of RCMs and six meteorological variables. Annual and monthly biases are reduced to close to zero by QM for all variables in most cases. Exceptions are found, if non-stationarity of the model’s error characteristics occur. Even in the worst cases of non-stationarity, QM clearly improves the biases of raw RCMs. In addition, QM successfully adjusts the distributions of the analysed variables. To approach the question whether time series and inter-variable relationships are still plausible after correction, we evaluate the root-mean-square error (RMSE), autocorrelation and inter-variable correlation. We found improvement or no clear effect in RMSE and autocorrelation, and no clear effect on the correlation between meteorological variables. These results demonstrate that QM retains the quality of the temporal structure in time series and the inter-variable dependencies of RCMs. It has to be emphasised that this cannot be interpreted as an improvement and that deficiencies of the RCMs in those features are retained as well. Our results give some indication for the performance of QM applied to future scenarios, since our evaluation relies on independent calibration and evaluation periods, which are affected by climate variability and change. The effect of non-stationarity, however, can be expected to be larger in far future. We demonstrate the retainment of the RCM’s temporal structure and inter-variable dependencies, and large improvements in biases. This qualifies QM as a valuable, though not perfect, method in the interface between climate models and climate change impact research. Nonetheless, in case of no correlation between re-analysis driven RCM and observation, one should consider that QM does not correct this correlation.