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- ItemThe Ice Selective Inlet: A novel technique for exclusive extraction of pristine ice crystals in mixed-phase clouds(München : European Geopyhsical Union, 2015) Kupiszewski, P.; Weingartner, E.; Vochezer, P.; Schnaiter, M.; Bigi, A.; Gysel, M.; Rosati, B.; Toprak, E.; Mertes, S.; Baltensperger, U.Climate predictions are affected by high uncertainties partially due to an insufficient knowledge of aerosol–cloud interactions. One of the poorly understood processes is formation of mixed-phase clouds (MPCs) via heterogeneous ice nucleation. Field measurements of the atmospheric ice phase in MPCs are challenging due to the presence of much more numerous liquid droplets. The Ice Selective Inlet (ISI), presented in this paper, is a novel inlet designed to selectively sample pristine ice crystals in mixed-phase clouds and extract the ice residual particles contained within the crystals for physical and chemical characterization. Using a modular setup composed of a cyclone impactor, droplet evaporation unit and pumped counterflow virtual impactor (PCVI), the ISI segregates particles based on their inertia and phase, exclusively extracting small ice particles between 5 and 20 μm in diameter. The setup also includes optical particle spectrometers for analysis of the number size distribution and shape of the sampled hydrometeors. The novelty of the ISI is a droplet evaporation unit, which separates liquid droplets and ice crystals in the airborne state, thus avoiding physical impaction of the hydrometeors and limiting potential artefacts. The design and validation of the droplet evaporation unit is based on modelling studies of droplet evaporation rates and computational fluid dynamics simulations of gas and particle flows through the unit. Prior to deployment in the field, an inter-comparison of the optical particle size spectrometers and a characterization of the transmission efficiency of the PCVI was conducted in the laboratory. The ISI was subsequently deployed during the Cloud and Aerosol Characterization Experiment (CLACE) 2013 and 2014 – two extensive international field campaigns encompassing comprehensive measurements of cloud microphysics, as well as bulk aerosol, ice residual and ice nuclei properties. The campaigns provided an important opportunity for a proof of concept of the inlet design. In this work we present the setup of the ISI, including the modelling and laboratory characterization of its components, as well as field measurements demonstrating the ISI performance and validating the working principle of the inlet. Finally, measurements of biological aerosol during a Saharan dust event (SDE) are presented, showing a first indication of enrichment of bio-material in sub-2 μm ice residuals.
- ItemTarget categorization of aerosol and clouds by continuous multiwavelength-polarization lidar measurements(Katlenburg-Lindau : Copernicus, 2017) Baars, Holger; Seifert, Patric; Engelmann, Ronny; Wandinger, UllaAbsolute calibrated signals at 532 and 1064 nm and the depolarization ratio from a multiwavelength lidar are used to categorize primary aerosol but also clouds in high temporal and spatial resolution. Automatically derived particle backscatter coefficient profiles in low temporal resolution (30 min) are applied to calibrate the lidar signals. From these calibrated lidar signals, new atmospheric parameters in temporally high resolution (quasi-particle-backscatter coefficients) are derived. By using thresholds obtained from multiyear, multisite EARLINET (European Aerosol Research Lidar Network) measurements, four aerosol classes (small; large, spherical; large, non-spherical; mixed, partly nonspherical) and several cloud classes (liquid, ice) are defined. Thus, particles are classified by their physical features (shape and size) instead of by source. The methodology is applied to 2 months of continuous observations (24 h a day, 7 days a week) with the multiwavelength-Raman-polarization lidar PollyXT during the High-Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)2) Observational Prototype Experiment (HOPE) in spring 2013. Cloudnet equipment was operated continuously directly next to the lidar and is used for comparison. By discussing three 24 h case studies, it is shown that the aerosol discrimination is very feasible and informative and gives a good complement to the Cloudnet target categorization. Performing the categorization for the 2-month data set of the entire HOPE campaign, almost 1 million pixel (5 min×30 m) could be analysed with the newly developed tool. We find that the majority of the aerosol trapped in the planetary boundary layer (PBL) was composed of small particles as expected for a heavily populated and industrialized area. Large, spherical aerosol was observed mostly at the top of the PBL and close to the identified cloud bases, indicating the importance of hygroscopic growth of the particles at high relative humidity. Interestingly, it is found that on several days non-spherical particles were dispersed from the ground into the atmosphere.
- ItemClassification of Arctic, midlatitude and tropical clouds in the mixed-phase temperature regime(Katlenburg-Lindau : EGU, 2017) Costa, Anja; Meyer, Jessica; Afchine, Armin; Luebke, Anna; Günther, Gebhard; Dorsey, James R.; Gallagher, Martin W.; Ehrlich, Andre; Wendisch, Manfred; Baumgardner, Darrel; Wex, Heike; Krämer, MartinaThe degree of glaciation of mixed-phase clouds constitutes one of the largest uncertainties in climate prediction. In order to better understand cloud glaciation, cloud spectrometer observations are presented in this paper, which were made in the mixed-phase temperature regime between 0 and -38°C (273 to 235K), where cloud particles can either be frozen or liquid. The extensive data set covers four airborne field campaigns providing a total of 139000 1Hz data points (38.6h within clouds) over Arctic, midlatitude and tropical regions. We develop algorithms, combining the information on number concentration, size and asphericity of the observed cloud particles to classify four cloud types: liquid clouds, clouds in which liquid droplets and ice crystals coexist, fully glaciated clouds after the Wegener-Bergeron-Findeisen process and clouds where secondary ice formation occurred. We quantify the occurrence of these cloud groups depending on the geographical region and temperature and find that liquid clouds dominate our measurements during the Arctic spring, while clouds dominated by the Wegener-Bergeron-Findeisen process are most common in midlatitude spring. The coexistence of liquid water and ice crystals is found over the whole mixed-phase temperature range in tropical convective towers in the dry season. Secondary ice is found at midlatitudes at -5 to -10°C (268 to 263K) and at higher altitudes, i.e. lower temperatures in the tropics. The distribution of the cloud types with decreasing temperature is shown to be consistent with the theory of evolution of mixed-phase clouds. With this study, we aim to contribute to a large statistical database on cloud types in the mixed-phase temperature regime.
- ItemRegional projections of temperature and precipitation changes: Robustness and uncertainty aspects(Stuttgart : Gebrueder Borntraeger Verlagsbuchhandlung, 2017) Piniewski, M.; Mezghani, A.; Szczésniak, M.; Kundzewicz, Z.W.This study presents the analysis of bias-corrected projections of changes in temperature and precipitation in the Vistula and Odra basins, covering approximately 90% of the Polish territory and small parts of neighbouring countries in Central and Eastern Europe. The ensemble of climate projections consists of nine regional climate model simulations from the EURO-CORDEX ensemble for two future periods 2021-2050 and 2071-2100, assuming two representative concentration pathways (RCPs) 4.5 and 8.5. The robustness is measured by the ensemble models' agreement on significant changes.We found a robust increase in the annual mean of daily minimum and maximum temperature, by 1-1.4 °C in the near future and by 1.9-3.8 °C in the far future (areal-means of the ensemble mean values). Higher increases are consistently associated with minimum temperature and the gradient of change goes from SWto NE regions. Seasonal projections of both temperature variables reflect lower robustness and suggest a higher future increase in winter temperatures than in other seasons, notably in the far future under RCP 8.5 (by more than 1 °C). However, changes in annual means of precipitation are uncertain and not robust in any of the analysed cases, even though the climate models agree well on the increase. This increase is intensified with rising global temperatures and varies from 5.5% in the near future under RCP 4.5 to 15.2%in the far future under RCP 8.5. Spatial variability is substantial, although quite variable between individual climate model simulations. Although seasonal means of precipitation are projected to considerably increase in all four combinations of RCPs and projection horizons for winter and spring, the high model spread reduces considerably the robustness, especially for the far future. In contrast, the ensemble members agree well that overall, the summer and autumn (with exception of the far future under RCP 8.5) precipitation will not undergo statistically significant changes.
- ItemImplications of potentially lower climate sensitivity on climate projections and policy(Bristol : IOP Publishing, 2014) Rogelj, Joeri; Meinshausen, Malte; Sedláček, Jan; Knutti, RetoClimate sensitivity, the long-term temperature response to CO2, has been notoriously difficult to constrain until today. Estimates based on the observed warming trends favor lower values, while the skill with which comprehensive climate models are able to simulate present day climate implies higher values to be more plausible. We find that much lower values would postpone crossing the 2 °C temperature threshold by about a decade for emissions near current levels, or alternatively would imply that limiting warming to below 1.5 °C would require about the same emission reductions as are now assumed for 2 °C. It is just as plausible, however, for climate sensitivity to be at the upper end of the consensus range. To stabilize global-mean temperature at levels of 2 °C or lower, strong reductions of greenhouse gas emissions in order to stay within the allowed carbon budget seem therefore unavoidable over the 21st century. Early reductions and the required phase-out of unabated fossil fuel emissions would be an important societal challenge. However, erring on the side of caution reduces the risk that future generations will face either the need for even larger emission reductions or very high climate change impacts.
- ItemCharacterizing the evolution of climate networks(Göttingen : Copernicus GmbH, 2014) Tupikina, L.; Rehfeld, K.; Molkenthin, N.; Stolbova, V.; Marwan, N.; Kurths, J.Complex network theory has been successfully applied to understand the structural and functional topology of many dynamical systems from nature, society and technology. Many properties of these systems change over time, and, consequently, networks reconstructed from them will, too. However, although static and temporally changing networks have been studied extensively, methods to quantify their robustness as they evolve in time are lacking. In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure. Our main result is the common component evolution function (CCEF) which characterizes network development over time. To test our approach we apply it to several model systems, ErdA's-Rényi networks, analytically derived flow-based networks, and transient simulations from the START model for which we control the change of single parameters over time. Then we construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain for the time period of 1970-2011 CE and use the CCEF to characterize the temporal evolution in this region. While this real-world CCEF displays a high degree of network persistence over large time lags, there are distinct time periods when common links break down. This phasing of these events coincides with years of strong El Niño/Southern Oscillation phenomena, confirming previous studies. The proposed method can be applied for any type of evolving network where the link but not the node set is changing, and may be particularly useful to characterize nonstationary evolving systems using complex networks.
- ItemS2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts(Malden, MA : Wiley-Blackwell, 2018) Cohen, Judah; Coumou, Dim; Hwang, Jessica; Mackey, Lester; Orenstein, Paulo; Totz, Sonja; Tziperman, EliThe discipline of seasonal climate prediction began as an exercise in simple statistical techniques. However, today the large government forecast centers almost exclusively rely on complex fully coupled dynamical forecast systems for their subseasonal to seasonal (S2S) predictions while statistical techniques are mostly neglected and those techniques still in use have not been updated in decades. In this Opinion Article, we argue that new statistical techniques mostly developed outside the field of climate science, collectively referred to as machine learning, can be adopted by climate forecasters to increase the accuracy of S2S predictions. We present an example of where unsupervised learning demonstrates higher accuracy in a seasonal prediction than the state-of-the-art dynamical systems. We also summarize some relevant machine learning methods that are most applicable to climate prediction. Finally, we show by comparing real-time dynamical model forecasts with observations from winter 2017/2018 that dynamical model forecasts are almost entirely insensitive to polar vortex (PV) variability and the impact on sensible weather. Instead, statistical forecasts more accurately predicted the resultant sensible weather from a mid-winter PV disruption than the dynamical forecasts. The important implication from the poor dynamical forecasts is that if Arctic change influences mid-latitude weather through PV variability, then the ability of dynamical models to demonstrate the existence of such a pathway is compromised. We conclude by suggesting that S2S prediction will be most beneficial to the public by incorporating mixed or a hybrid of dynamical forecasts and updated statistical techniques such as machine learning.