Добірка наукової літератури з теми "Arctic, Atmospheric Energy Fluxes, Self-organizing Maps"

The meridional temperature gradient between mid and high latitudes decreases by Arctic amplification. Following this decrease, the circulation in the mid latitudes may change and, therefore, the meridional flux of heat and moisture increases. This might increase the Arctic temperatures even further. A proxy for the vertically integrated atmospheric horizontal energy flux was analyzed using the self-organizing-map (SOM) method. Climate Model Intercomparison Project Phase 5 (CMIP5) model data of the historical and Representative Concentration Pathway 8.5 (RCP8.5) experiments were analyzed to extract horizontal flux patterns. These patterns were analyzed for changes between and within the respective experiments. It was found that the general horizontal flux patterns are reproduced by all models and in all experiments in comparison with reanalyses. By comparing the reanalysis time frame with the respective historical experiments, we found that the general occurrence frequencies of the patterns differ substantially. The results show that the general structure of the flux patterns is not changed when comparing the historical and RCP8.5 results. However, the amplitudes of the fluxes are decreasing. It is suggested that the amplitudes are smaller in the RCP8.5 results compared to the historical results, following a greater meandering of the jet stream, which yields smaller flux amplitudes of the cluster mean.

The Greenland Ice Sheet (GrIS) plays a crucial role in the Arctic climate, and atmospheric conditions are the primary modifier of mass balance. This analysis establishes the relationship between large-scale atmospheric circulation and principal determinants of GrIS mass balance: moisture, cloud properties, radiative forcing, and temperature. Using self-organizing maps (SOMs), observations from the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit (ICECAPS) project are categorized by daily sea level pressure (SLP) gradient. The results describe in detail how southerly, northerly, and zonal circulation regimes impact observations at Summit Station, Greenland. This southerly regime is linked to large anomalous increases in low-level liquid cloud formation, cloud radiative forcing (CRF), and surface warming at Summit Station. An individual southerly pattern relates to the largest positive anomalies, with the most extreme 25% of cases leading to CRF anomalies above 21 W m−2 and temperature anomalies beyond 8.5°C. Finally, the July 2012 extreme melt event is analyzed, showing that the prolonged ice sheet warming was related to persistence of these southerly circulation patterns, causing an unusually extended period of anomalous CRF and temperature. These results demonstrate a novel methodology, connecting daily atmospheric circulation to a relatively brief record of observations.

AbstractThe wintertime (December–February) 1990–2016 Arctic surface air temperature (SAT) trend is examined using self-organizing maps (SOMs). The high-dimensional SAT dataset is reduced into nine representative SOM patterns, with each pattern exhibiting a decorrelation time scale of about 10 days and having about 85% of its variance coming from intraseasonal time scales. The trend in the frequency of occurrence of each SOM pattern is used to estimate the interdecadal Arctic winter warming trend associated with the SOM patterns. It is found that trends in the SOM patterns explain about one-half of the SAT trend in the Barents and Kara Seas, one-third of the SAT trend around Baffin Bay, and two-thirds of the SAT trend in the Chukchi Sea. A composite calculation of each term in the thermodynamic energy equation for each SOM pattern shows that the SAT anomalies grow primarily through the advection of the climatological temperature by the anomalous wind. This implies that a substantial fraction of Arctic amplification is due to horizontal temperature advection that is driven by changes in the atmospheric circulation. An analysis of the surface energy budget indicates that the skin temperature anomalies as well as the trend, although very similar to that of the SAT, are produced primarily by downward longwave radiation.

Abstract. Arctic amplification causes the meridional temperature gradient between middle and high latitudes to decrease. Through this decrease the large-scale circulation in the midlatitudes may change and therefore the meridional transport of heat and moisture increases. This in turn may increase Arctic warming even further. To investigate patterns of Arctic temperature, horizontal transports and their changes in time, we analysed ERA-Interim daily winter data of vertically integrated horizontal moist static energy transport using self-organizing maps (SOMs). Three general transport pathways have been identified: the North Atlantic pathway with transport mainly over the northern Atlantic, the North Pacific pathway with transport from the Pacific region, and the Siberian pathway with transport towards the Arctic over the eastern Siberian region. Transports that originate from the North Pacific are connected to negative temperature anomalies over the central Arctic. These North Pacific pathways have been becoming less frequent during the last decades. Patterns with origin of transport in Siberia are found to have no trend and show cold temperature anomalies north of Svalbard. It was found that transport patterns that favour transport through the North Atlantic into the central Arctic are connected to positive temperature anomalies over large regions of the Arctic. These temperature anomalies resemble the warm Arctic–cold continents pattern. Further, it could be shown that transport through the North Atlantic has been becoming more frequent during the last decades.

The effects of rapid Arctic warming and ice loss on weather patterns in the Northern Hemisphere is a topic of active research, lively scientific debate and high societal impact. The emergence of Arctic amplification—the enhanced sensitivity of high-latitude temperature to global warming—in only the last 10–20 years presents a challenge to identifying statistically robust atmospheric responses using observations. Several recent studies have proposed and demonstrated new mechanisms by which the changing Arctic may be affecting weather patterns in mid-latitudes, and these linkages differ fundamentally from tropics/jet-stream interactions through the transfer of wave energy. In this study, new metrics and evidence are presented that suggest disproportionate Arctic warming—and resulting weakening of the poleward temperature gradient—is causing the Northern Hemisphere circulation to assume a more meridional character (i.e. wavier), although not uniformly in space or by season, and that highly amplified jet-stream patterns are occurring more frequently. Further analysis based on self-organizing maps supports this finding. These changes in circulation are expected to lead to persistent weather patterns that are known to cause extreme weather events. As emissions of greenhouse gases continue unabated, therefore, the continued amplification of Arctic warming should favour an increased occurrence of extreme events caused by prolonged weather conditions.

Abstract Accurate representation of the stability of the surface layer in numerical weather prediction models is important because of the impact it has on forecasts of surface energy, moisture, and momentum fluxes. It also impacts boundary layer processes such as the generation of turbulence, the creation of near-surface flows, and fog formation. This paper uses observations from a 30-m automatic weather station on the Ross Ice Shelf, Antarctica, to evaluate the near-surface layer in the Antarctic Mesoscale Prediction System (AMPS), a numerical weather prediction system used for forecasting in Antarctica. The method of self-organizing maps (SOM) is used to identify characteristic potential temperature anomaly profiles observed at the 30-m tower. The SOM-identified profiles are then used to evaluate the performance of AMPS as a function of atmospheric stability. The results indicate AMPS underpredicts the frequency of near-neutral profiles and instead overpredicts the frequency of weakly unstable and weak to moderately stable profiles. AMPS does not forecast the strongest statically stable patterns observed by Tall Tower, but in the median, the AMPS forecasts are more statically stable across all wind speeds, indicating a possible mechanical mixing error or a negative radiation bias. The SOM analysis identifies a negative radiation bias under near-neutral to weakly stable conditions, causing an overrepresentation of the static stability in AMPS. AMPS has a positive wind speed bias in moderate to strongly stable conditions, which generates too much mechanical mixing and an underrepresentation of the static stability. Model errors increase with increasing atmospheric stability.

The meridional temperature gradient between middle and high latitudes is decreasing due to Arctic amplification, which enhances the warming in the Arctic region. This change in temperature is also influencing the circulation and the horizontal energy fluxes between the mid latitudes and the Arctic, which itself might influence the Arctic additionally. The horizontal energy flux, to our best knowledge, has never been analyzed using the up-to-date method called self-organizing map (SOM). The SOM is a simple unsupervised neural network that is used to extract patterns of high-dimensional data and presents the patterns in a two dimensional lattice, where similar (more different) patterns are closer together (farther apart) within the lattice. An advantage of using the SOM is that there are no underlying linear assumptions like in other methods that characterize the circulation, such as the Arctic Oscillation or the North Atlantic Oscillation index. The SOM has been used in this work to extract and analyze horizontal heat flux patterns from reanalysis data and climate model data. Using the SOM method, it was possible to find distinct horizontal heat flux patterns into the Arctic, that have been combined into heat flux pathways. The SOM made it possible to characterize the pathways' change in occurrence frequency throughout the last thirty years and the change between present-day climate model simulations and climate projections with increased greenhouse gas concentrations. Using reanalysis data, three distinct patterns have been extracted, which all show different features. They are named according to the main pathway the horizontal heat flux takes to reach the Arctic: the Atlantic pathway, the Pacific pathway, and the continental pathway. For the reanalysis data, it is shown that the Atlantic pathway, which is connected with positive temperature anomalies in the central Arctic, has become more frequent during the last three decades, while the Pacific pathway, that is connected to negative temperature anomalies around Svalbard, has become less frequent. This suggests that the circulation, which is connected to the temperature in the Arctic, is changing. The trends for the occurrence frequencies of the SOM horizontal heat flux pathways have, to our best knowledge, never been analyzed prior to this work. With respect to climate model results, the three distinct patterns were also identified in climate simulations of the second half of the twentieth century and climate projections of the second half of the twenty-first century from eight models. This demonstrates that these three pathways are an inherent part of the atmosphere. In comparison with the reanalysis data, the climate models show much stronger occurrence frequencies for the continental pathway. The reanalysis data of the continental pathway does not show such high occurrence frequencies. However, the multi model mean shows a clear decrease in these occurrence frequencies of the continental pathway between the present-day climate simulation and the climate projection with increased greenhouse gas concentrations. The continental pathway is mostly connected to strong zonal fluxes while there are only small meridional transports over Siberia or North America. This suggests that the fluxes become more meridional with an enhanced warming and thus increase the heat flux into the Arctic, which might influence the surface air temperature.:Bibliographische Beschreibung Bibliographic Description Acronyms 1. Introduction: Arctic Amplification, Circulation and Transport 1.1. Arctic Amplification 1.2. The (AC)3 project 1.3. Overview of General Circulation in Mid and High Latitudes 1.3.1. Drivers of the general circulation 1.3.2. Circulation impacts on high and mid latitudes 1.3.3. Atmospheric energy transport into the Arctic 1.4. Overview of the Thesis 2. The Self-organizing Map 2.1. Mathematical Description 2.2. SOM Parameters and their Effect on Clustering Meteorological Data 2.2.1. Map size 2.2.2. Neighborhood function 2.2.3. Iterations 2.2.4. Learning rate 2.2.5. Summary of the effect of learning parameters 2.3. Limits of SOM 2.4. Application of SOM in Atmospheric Sciences 2.5. Comparison with the K-Means Clustering Algorithm 2.6. A Practical Guide to SOM 3. Clustering of Atmospheric Energy Transport within ERA-Interim 3.1. Data and Method 3.1.1. ERA-Interim data 3.1.2. Analysis method 3.2. Results 3.2.1. Heat transport SOM 3.2.2. Temperature anomaly composites related to transport pathways 3.2.3. Mean meridional heat transport 3.2.4. Trend of transport pathways 3.2.5. Two-meter temperature trends 3.3. Discussion 3.4. Summary of ERA-Interim Analysis 4. Comparison of Flux Pathways in CMIP5 Model Analysis 4.1. Methods and Data 4.1.1. CMIP5 model data 4.1.2. Analysis using the SOM method 4.2. Results 4.2.1. Historical patterns 4.2.2. RCP8.5 patterns 4.2.3. Mean pathway occurrence frequencies 4.2.4. Pathway occurrence frequency trends during the historical and future time intervals 4.3. Discussion of CMIP5 Analysis 5. Summary and Conclusion of the Horizontal Energy Flux SOM Analysis References A. Appendix: ERA-Interim Self-Organizing Map Analysis B. Appendix: CMIP5 Self-Organizing Map Results Acknowledgments Curriculum Vitae Affirmation
Der meridionale Temperaturgradient zwischen mittleren und hohen Breiten nimmt aufgrund der arktischen Verstärkung ab. Diese Temperaturänderung beeinflusst auch die Zirkulation und die horizontalen Energieflüsse zwischen den mittleren Breiten und der Arktis, was die Arktis selbst zusätzlich beeinflussen könnte. Der horizontale Energietransport wurde, unserem bestem Wissen nach, nie mit der aktuellen Methode namens Self-Organizing Map (SOM) analysiert. Die SOM ist ein einfaches unüberwachtes neuronales Netzwerk, das zum Extrahieren von Mustern hoch dimensionaler Daten verwendet wird und die Muster in einem zweidimensionalen Gitter darstellt, in dem ähnliche (unterschiedliche) Muster innerhalb des Gitters näher beieinander (weiter voneinander entfernt) liegen. Ein Vorteil der SOM besteht darin, dass keine linearen Annahmen wie bei anderen Methoden vorliegen, die die Zirkulation charakterisieren, wie z. B. die Arktische Oszillation oder der Nordatlantische Oszillationsindex. Die SOM wurde im Rahmen dieser Arbeit verwendet, um horizontale Wärmetransportmuster aus Reanalysedaten und Klimamodelldaten zu extrahieren und zu analysieren. Mit der SOM-Methode konnten unterschiedliche horizontale Muster des Wärmetransports in die Arktis identifiziert werden, welche wiederum zu Pfaden zusammengefasst wurden. Die SOM ermöglichte es, die Veränderung der Auftrittshäufigkeit der Pfade in den letzten dreißig Jahren und die Veränderung der Muster zwischen einer Simulation des heutigen Zustandes und einer Klimaprojektion mit erhöhten Treibhausgaskonzentrationen zu charakterisieren. Unter Verwendung von Reanalysedaten konnten drei unterschiedliche Pfade extrahiert werden, die alle unterschiedliche Merkmale aufweisen. Sie wurden nach dem jeweiligen Hauptpfad benannt, den der horizontale Wärmetransport vollzieht, um in die Arktis zu gelangen: der Atlantikpfad, der Pazifikpfad und der Kontinentalpfad. Für die Reanalysedaten konnte gezeigt werden, dass die Auftretenshäufigkeit des Atlantikpfads, der mit positiven Temperaturanomalien in der Zentralarktis verbunden ist, in den letzten drei Jahrzehnten gestiegen ist. Demgegenüber ist die Auftretenshäufigkeit des pazifischen Pfads, der mit negativen Temperaturanomalien um Spitzbergen verbunden ist, in den letzten drei Jahrzehnten gesunken. Dies deutet darauf hin, dass sich die Zirkulation, die mit der Temperatur in der Arktis verbunden ist, ändert. Die Trends für die Auftrittshäufigkeiten der horizontalen SOM-Wärmetransportpfade wurden, nach bestem Wissen, vor dieser Arbeit noch nie analysiert. Auswertungen basierend auf acht Klimamodellen haben die drei unterschiedlichen Muster sowohl in Klimasimulationen für die zweite Hälfte des zwanzigsten Jahrhunderts, als auch in Klimaprojektionen der zweiten Hälfte des einundzwanzigsten Jahrhunderts gefunden. Dies zeigt, dass diese drei Pfade der Atmosphäre inhärent sind. Im Vergleich zu den Reanalysedaten zeigen die Klimamodelle viel stärkere Auftrittshäufigkeiten für den Kontinentalpfad. Die Reanalysedaten des Kontinentalpfads weisen keine hohen Auftrittshäufigkeiten auf. Der Multi-Modell-Mittelwert zeigt jedoch eine deutliche Abnahme dieser Auftrittshäufigkeiten des Kontinentalpfads zwischen der Simulation des heutigen Zustands und der Projektion mit erhöhten Treibhausgaskonzentrationen. Der Kontinentalpfad ist meist mit starken zonalen Transporten verbunden, während nur kleine meridionale Transporte über Sibirien oder Nordamerika erfolgen. Dies deutet darauf hin, dass mit zunehmender Erwärmung die Flüsse meridionaler werden sowie den Wärmetransport in die Arktis erhöhen und somit die Lufttemperatur in Bodennähe beeinflussen können.:Bibliographische Beschreibung Bibliographic Description Acronyms 1. Introduction: Arctic Amplification, Circulation and Transport 1.1. Arctic Amplification 1.2. The (AC)3 project 1.3. Overview of General Circulation in Mid and High Latitudes 1.3.1. Drivers of the general circulation 1.3.2. Circulation impacts on high and mid latitudes 1.3.3. Atmospheric energy transport into the Arctic 1.4. Overview of the Thesis 2. The Self-organizing Map 2.1. Mathematical Description 2.2. SOM Parameters and their Effect on Clustering Meteorological Data 2.2.1. Map size 2.2.2. Neighborhood function 2.2.3. Iterations 2.2.4. Learning rate 2.2.5. Summary of the effect of learning parameters 2.3. Limits of SOM 2.4. Application of SOM in Atmospheric Sciences 2.5. Comparison with the K-Means Clustering Algorithm 2.6. A Practical Guide to SOM 3. Clustering of Atmospheric Energy Transport within ERA-Interim 3.1. Data and Method 3.1.1. ERA-Interim data 3.1.2. Analysis method 3.2. Results 3.2.1. Heat transport SOM 3.2.2. Temperature anomaly composites related to transport pathways 3.2.3. Mean meridional heat transport 3.2.4. Trend of transport pathways 3.2.5. Two-meter temperature trends 3.3. Discussion 3.4. Summary of ERA-Interim Analysis 4. Comparison of Flux Pathways in CMIP5 Model Analysis 4.1. Methods and Data 4.1.1. CMIP5 model data 4.1.2. Analysis using the SOM method 4.2. Results 4.2.1. Historical patterns 4.2.2. RCP8.5 patterns 4.2.3. Mean pathway occurrence frequencies 4.2.4. Pathway occurrence frequency trends during the historical and future time intervals 4.3. Discussion of CMIP5 Analysis 5. Summary and Conclusion of the Horizontal Energy Flux SOM Analysis References A. Appendix: ERA-Interim Self-Organizing Map Analysis B. Appendix: CMIP5 Self-Organizing Map Results Acknowledgments Curriculum Vitae Affirmation