Analysis of meso- and microscale hydrometeorological fluxes in TERENO preAlpine using WRF-LES

A Large-Eddy Simulation (LES) using the Weather Research and Forecasting (WRF) model is set up in a computationally efficient way, directly driving the single domain with reanalysis data as boundary conditions. The simulation represents two real episodes over a well-known and real area. It is shown that the model successfully produces turbulent structures as they are known from idealized LES in literature and that the inertial subrange of the turbulence spectrum is appropriately resolved. The simulated wind field is evaluated with measurements taken during the ScaleX-campaigns by a triple Doppler Lidar setup that can measure all three wind components with a high temporal and vertical resolution throughout the atmospheric boundary layer. Model results sufficiently recreate the measured wind speed and direction as well as the development of daytime and nocturnal boundary layers. The coarse spatial and temporal resolution of the boundary conditions limits the accuracy of the model, shown by the representation of low-level jets. A katabatic flow reveals that the model successfully produces local weather phenomena that are not present in the boundary conditions and proves that the model output can be considered as a four-dimensional representation of the flow structures for a known area. This is not achievable with measurements. The implementation of realistic soil information (moisture and temperature) allows for a simulation of the sensible and latent heat fluxes. The advantage of the model over measurements here lies in the possibility to evaluate the turbulent fluxes at every location and height and the chance to evaluate the dependence of the fluxes on the soil properties below. The presented setup can be used to gather in-depth knowledge of the small-scale flow structures in a known area or to generalize soil-atmosphere interactions for large-area climate models.Die Dissertation beschreibt die Rechenzeit-effiziente Realisierung und Analyse einer large eddy simulation mit dem Weather Research and Forecasting Modell, bei der die meteorologischen Randbedingungen für die einzelne Domain direkt aus Reanalysedaten abgeleitet werden. Die Simulation erstreckt sich über zwei reale 48-Stunden lange Perioden in einem realen Gebiet. Das Modell produziert genau die turbulenten Strukturen, die aus idealisierten Simulationen aus der Literatur bekannt sind. Die inertial subrange ist deutlich zu erkennen. Messdaten von einem aus drei Doppler Lidar-Geräten bestehenden virtuellen Messturm, der die drei Windkomponenten in hoher zeitlicher und vertikaler Auflösung messen kann und während der ScaleX-Messkampagnen zum Einsatz kam, dienen zur Evaluierung des Modells. Gemessene Windgeschwindigkeiten und -richtungen werden im Modell gut abgebildet; die Grenzschichtentwicklung bei Tag und Nacht ist angemessen repräsentiert. Limitierungen zeigen sich in der Abbildung der gemessenen low-level jets, deren Genauigkeit durch die unzureichende räumliche und zeitliche Auflösung der Randbedingungen begrenzt ist. Am Beispiel eines katabatischen Kaltluftabflusses wird gezeigt, dass das Modell mikrometeorologische Phänomene erzeugt, die nicht aus den Randbedingungen stammen. Das bedeutet, dass die Modellergebnisse ein vierdimensionales Abbild der Strömungsverhältnisse in einem realen Gebiet darstellen. Mit Messungen ist das nicht erreichbar. Durch die Implementierung gemessener Bodenfeuchtigkeit und -temperatur in das Modell lassen sich realistische latente und sensible Wärmeströme berechnen. Im Modell können diese, im Gegensatz zu Messungen, an jedem Ort und in jeder Höhe bestimmt werden und die Abhängigkeit von der Bodenbeschaffenheit wird beschreibbar. Der gezeigte Modellansatz kann zur Untersuchung von kleinräumigen Strömungsmustern oder zur besseren Beschreibung kleinskaliger Effekte von Boden-Atmosphäre Wechselwirkungen in gröber aufgelösten Modellen verwendet werden

Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications

A multivariate bias correction based on N-dimensional probability density function transform (MBCn) technique is applied to four different high-resolution regional climate change simulations and key meteorological variables, namely precipitation, mean near-surface air temperature, near-surface maximum air temperature, near-surface minimum air temperature, surface downwelling solar radiation, relative humidity, and wind speed. The impact of bias-correction on the historical (1980–2005) period, the inter-variable relationships, and the measures of spatio-temporal consistency are investigated. The focus is on the discrepancies between the original and the bias-corrected results over five agro-ecological zones. We also evaluate relevant indices for agricultural applications such as climate extreme indices, under current and future (2020–2050) climate change conditions based on the RCP4.5. Results show that MBCn successfully corrects the seasonal biases in spatial patterns and intensities for all variables, their intervariable correlation, and the distributions of most of the analyzed variables. Relatively large bias reductions during the historical period give indication of possible benefits of MBCn when applied to future scenarios. Although the four regional climate models do not agree on the same positive/negative sign of the change of the seven climate variables for all grid points, the model ensemble mean shows a statistically significant change in rainfall, relative humidity in the Northern zone and wind speed in the Coastal zone of West Africa and increasing maximum summer temperature up to 2°C in the Sahara

Multivariate bias‐correction of high‐resolution regional climate change simulations for West Africa: performance and climate change implications

A multivariate bias correction based on N‐dimensional probability density function transform (MBCn) technique is applied to four different high‐resolution regional climate change simulations and key meteorological variables, namely precipitation, mean near‐surface air temperature, near‐surface maximum air temperature, near‐surface minimum air temperature, surface downwelling solar radiation, relative humidity, and wind speed. The impact of bias‐correction on the historical (1980–2005) period, the inter‐variable relationships, and the measures of spatio‐temporal consistency are investigated. The focus is on the discrepancies between the original and the bias‐corrected results over five agro‐ecological zones. We also evaluate relevant indices for agricultural applications such as climate extreme indices, under current and future (2020–2050) climate change conditions based on the RCP4.5. Results show that MBCn successfully corrects the seasonal biases in spatial patterns and intensities for all variables, their intervariable correlation, and the distributions of most of the analyzed variables. Relatively large bias reductions during the historical period give indication of possible benefits of MBCn when applied to future scenarios. Although the four regional climate models do not agree on the same positive/negative sign of the change of the seven climate variables for all grid points, the model ensemble mean shows a statistically significant change in rainfall, relative humidity in the Northern zone and wind speed in the Coastal zone of West Africa and increasing maximum summer temperature up to 2°C in the Sahara

A new multi-proxy investigation of Late Quaternary palaeoenvironments along the north-western Barents Sea (Storfjorden Trough Mouth Fan)

A new integrated micropalaeontological study on planktonic and benthic foraminifera, calcareous nannofossils and diatoms was performed on three sediment cores from the Storfjorden Trough Mouth Fan to reconstruct the Late Quaternary palaeoenvironmental and climatic history. Two main intervals were discussed: the last deglaciation (16.2\u201311.7 ka BP) and the Holocene. The age model relies on palaeomagnetic parameters together with 10 radiocarbon dates. Deglacial sediments had largely diluted the biogenic content which was scarce and poorly preserved. The first occurrence of Cibicidoides wuellerstorfi (benthic foraminifer), together with Turborotalita quinqueloba (planktonic foraminifer) and Coscinodiscus spp. (diatoms) at 11.3 ka BP followed the end of the Younger Dryas cold event and marked the beginning of the early Holocene warm period. Diatoms and planktonic foraminifers indicated a warming of the surface water from 10.5 to 9.2 ka BP, identifying the Holocene Thermal Maximum event. Bottom water fauna registered these warming conditions less clearly. Cooling events were identified during the Holocene, in particular the 8.2 ka BP event and the Neoglacial between 3.2 and 2 ka BP, as shown by the presence of cold-water taxa such as Gephyrocapsa muellerae (nannoplankton) and Neogloboquadrina pachyderma (planktonic foraminifer). These events were influenced by sea ice extent, cold or relatively warm current influxes

Linking the Modern Distribution of Biogenic Proxies in High Arctic Greenland Shelf Sediments to Sea Ice, Primary Production, and Arctic-Atlantic Inflow

The eastern north coast of Greenland is considered to be highly sensitive to the ongoing Arctic warming, but there is a general lack of data on modern conditions and in particular on the modern distribution of climate and environmental proxies to provide a baseline and context for studies on past variability. Here we present a detailed investigation of 11 biogenic proxies preserved in surface sediments from the remote High Arctic Wandel Sea shelf, the entrance to the Independence, Hagen, and Danmark fjords. The composition of organic matter (organic carbon, C:N ratios, C-13, N-15, biogenic silica, and IP25) and microfossil assemblages revealed an overall low primary production dominated by benthic diatoms, especially at the shallow sites. While the benthic and planktic foraminiferal assemblages underline the intrusion of chilled Atlantic waters into the deeper parts of the study area, the distribution of organic-walled dinoflagellate cysts is controlled by the local bathymetry and sea ice conditions. The distribution of the dinoflagellate cyst Polarella glacialis matches that of seasonal sea ice and the specific biomarker IP25, highlighting the potential of this species for paleo sea ice studies. The information inferred from our multiproxy study has important implications for the interpretation of the biogenic-proxy signal preserved in sediments from circum-Arctic fjords and shelf regions and can serve as a baseline for future studies. This is the first study of its kind in this area.Peer reviewe