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- ItemThe Global Aerosol Synthesis and Science Project (GASSP): Measurements and Modeling to Reduce Uncertainty(Boston, Mass. : ASM, 2017) Reddington, C.L.; Carslaw, K.S.; Stier, P.; Schutgens, N.; Coe, H.; Liu, D.; Allan, J.; Browse, J.; Pringle, K.J.; Lee, L.A.; Yoshioka, M.; Johnson, J.S.; Regayre, L.A.; Spracklen, D.V.; Mann, G.W.; Clarke, A.; Hermann, M.; Henning, S.; Wex, H.; Kristensen, T.B.; Leaitch, W.R.; Pöschl, U.; Rose, D.; Andreae, M.O.; Schmale, J.; Kondo, Y.; Oshima, N.; Schwarz, J.P.; Nenes, A.; Anderson, B.; Roberts, G.C.; Snider, J.R.; Leck, C.; Quinn, P.K.; Chi, X.; Ding, A.; Jimenez, J.L.; Zhang, Q.The largest uncertainty in the historical radiative forcing of climate is caused by changes in aerosol particles due to anthropogenic activity. Sophisticated aerosol microphysics processes have been included in many climate models in an effort to reduce the uncertainty. However, the models are very challenging to evaluate and constrain because they require extensive in situ measurements of the particle size distribution, number concentration, and chemical composition that are not available from global satellite observations. The Global Aerosol Synthesis and Science Project (GASSP) aims to improve the robustness of global aerosol models by combining new methodologies for quantifying model uncertainty, to create an extensive global dataset of aerosol in situ microphysical and chemical measurements, and to develop new ways to assess the uncertainty associated with comparing sparse point measurements with low-resolution models. GASSP has assembled over 45,000 hours of measurements from ships and aircraft as well as data from over 350 ground stations. The measurements have been harmonized into a standardized format that is easily used by modelers and nonspecialist users. Available measurements are extensive, but they are biased to polluted regions of the Northern Hemisphere, leaving large pristine regions and many continental areas poorly sampled. The aerosol radiative forcing uncertainty can be reduced using a rigorous model–data synthesis approach. Nevertheless, our research highlights significant remaining challenges because of the difficulty of constraining many interwoven model uncertainties simultaneously. Although the physical realism of global aerosol models still needs to be improved, the uncertainty in aerosol radiative forcing will be reduced most effectively by systematically and rigorously constraining the models using extensive syntheses of measurements.
- ItemReply to Burgess et al: Catastrophic climate risks are neglected, plausible, and safe to study(Washington, DC : National Acad. of Sciences, 2022) Kemp, Luke; Xu, Chi; Depledge, Joanna; Ebi, Kristie L.; Gibbins, Goodwin; Kohler, Timothy A.; Rockström, Johan; Scheffer, Marten; Schellnhuber, Hans Joachim; Steffen, Will; Lenton, Timothy M.
- ItemInterfacial photochemistry at the ocean surface is a global source of organic vapors and aerosols([London] : Nature Publishing Group UK, 2018) Brüggemann, Martin; Hayeck, Nathalie; George, ChristianThe surface of the oceans acts as a global sink and source for trace gases and aerosol particles. Recent studies suggest that photochemical reactions at this air/water interface produce organic vapors, enhancing particle formation in the atmosphere. However, current model calculations neglect this abiotic source of reactive compounds and account only for biological emissions. Here we show that interfacial photochemistry serves as a major abiotic source of volatile organic compounds (VOCs) on a global scale, capable to compete with emissions from marine biology. Our results indicate global emissions of 46.4-184 Tg C yr-1 of organic vapors from the oceans into the marine atmosphere and a potential contribution to organic aerosol mass of more than 60% over the remote ocean. Moreover, we provide global distributions of VOC formation potentials, which can be used as simple tools for field studies to estimate photochemical VOC emissions depending on location and season.
- ItemGlobal perturbation of stratospheric water and aerosol burden by Hunga eruption(London : Springer Nature, 2022) Khaykin, Sergey; Podglajen, Aurelien; Ploeger, Felix; Grooß, Jens-Uwe; Tence, Florent; Bekki, Slimane; Khlopenkov, Konstantin; Bedka, Kristopher; Rieger, Landon; Baron, Alexandre; Godin-Beekmann, Sophie; Legras, Bernard; Sellitto, Pasquale; Sakai, Tetsu; Barnes, John; Uchino, Osamu; Morino, Isamu; Nagai, Tomohiro; Wing, Robin; Baumgarten, Gerd; Gerding, Michael; Duflot, Valentin; Payen, Guillaume; Jumelet, Julien; Querel, Richard; Liley, Ben; Bourassa, Adam; Clouser, Benjamin; Feofilov, Artem; Hauchecorne, Alain; Ravetta, FrançoisThe eruption of the submarine Hunga volcano in January 2022 was associated with a powerful blast that injected volcanic material to altitudes up to 58 km. From a combination of various types of satellite and ground-based observations supported by transport modeling, we show evidence for an unprecedented increase in the global stratospheric water mass by 13% relative to climatological levels, and a 5-fold increase of stratospheric aerosol load, the highest in the last three decades. Owing to the extreme injection altitude, the volcanic plume circumnavigated the Earth in only 1 week and dispersed nearly pole-to-pole in three months. The unique nature and magnitude of the global stratospheric perturbation by the Hunga eruption ranks it among the most remarkable climatic events in the modern observation era, with a range of potential long-lasting repercussions for stratospheric composition and climate.
- ItemReply to Kelman: The foundations for studying catastrophic climate risks(Washington, DC : National Acad. of Sciences, 2022) Kemp, Luke; Xu, Chi; Depledge, Joanna; Ebi, Kristie L.; Gibbins, Goodwin; Kohler, Timothy A.; Rockström, Johan; Scheffer, Marten; Schellnhuber, Hans Joachim; Steffen, Will; Lenton, Timothy M.
- ItemCRAAS: A European Cloud Regime dAtAset Based on the CLAAS-2.1 Climate Data Record(Basel : MDPI, 2022) Tzallas, Vasileios; Hünerbein, Anja; Stengel, Martin; Meirink, Jan Fokke; Benas, Nikos; Trentmann, Jörg; Macke, AndreasGiven the important role of clouds in our planet’s climate system, it is crucial to further improve our understanding of their governing processes as well as the resulting spatio-temporal variability of their properties. This co-variability of different cloud optical properties is adequately represented through the well-established concept of cloud regimes. The focus of the present study lies on the creation of a cloud regime dataset over Europe, named “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS), in order to analyze their variability and their changes at different spatio-temporal scales. In addition, co-occurrences between the cloud regimes and large-scale weather patterns are investigated. The CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the Satellite Application Facility on Climate Monitoring (CM SAF), was used as the basis for the derivation of the cloud regimes over Europe for a 14-year period (2004–2017). In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 were used in order to compute 2D histograms. Then, the k-means clustering algorithm was applied to the generated 2D histograms in order to derive the cloud regimes. Eight cloud regimes were identified, which, along with the geographical distribution of their frequency of occurrence, assisted in providing a detailed description of the climate of the cloud properties over Europe. The annual and diurnal variabilities of the eight cloud regimes were studied, and trends in their frequency of occurrence were also examined. Larger changes in the frequency of occurrence of the produced cloud regimes were found for a regime associated to alto- and nimbo-type clouds and for a regime connected to shallow cumulus clouds and fog (−0.65% and +0.70% for the time period of the study, respectively).
- ItemDecay radius of climate decision for solar panels in the city of Fresno, USA([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Barton-Henry, Kelsey; Wenz, Leonie; Levermann, AndersTo design incentives towards achieving climate mitigation targets, it is important to understand the mechanisms that affect individual climate decisions such as solar panel installation. It has been shown that peer effects are important in determining the uptake and spread of household photovoltaic installations. Due to coarse geographical data, it remains unclear whether this effect is generated through geographical proximity or within groups exhibiting similar characteristics. Here we show that geographical proximity is the most important predictor of solar panel implementation, and that peer effects diminish with distance. Using satellite imagery, we build a unique geo-located dataset for the city of Fresno to specify the importance of small distances. Employing machine learning techniques, we find the density of solar panels within the shortest measured radius of an address is the most important factor in determining the likelihood of that address having a solar panel. The importance of geographical proximity decreases with distance following an exponential curve with a decay radius of 210 meters. The dependence is slightly more pronounced in low-income groups. These findings support the model of distance-related social diffusion, and suggest priority should be given to seeding panels in areas where few exist.
- ItemMonsoon forced evolution of savanna and the spread of agro-pastoralism in peninsular India([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Riedel, Nils; Fuller, Dorian Q.; Marwan, Norbert; Poretschkin, Constantin; Basavaiah, Nathani; Menzel, Philip; Ratnam, Jayashree; Prasad, Sushma; Sachse, Dirk; Sankaran, Mahesh; Sarkar, Saswati; Stebich, MartinaAn unresolved issue in the vegetation ecology of the Indian subcontinent is whether its savannas, characterized by relatively open formations of deciduous trees in C4-grass dominated understories, are natural or anthropogenic. Historically, these ecosystems have widely been regarded as anthropogenic-derived, degraded descendants of deciduous forests. Despite recent work showing that modern savannas in the subcontinent fall within established bioclimatic envelopes of extant savannas elsewhere, the debate persists, at least in part because the regions where savannas occur also have a long history of human presence and habitat modification. Here we show for the first time, using multiple proxies for vegetation, climate and disturbances from high-resolution, well-dated lake sediments from Lonar Crater in peninsular India, that neither anthropogenic impact nor fire regime shifts, but monsoon weakening during the past ~ 6.0 kyr cal. BP, drove the expansion of savanna at the expense of forests in peninsular India. Our results provide unambiguous evidence for a climate-induced origin and spread of the modern savannas of peninsular India at around the mid-Holocene. We further propose that this savannization preceded and drove the introduction of agriculture and development of sedentism in this region, rather than vice-versa as has often been assumed.
- ItemRecurrence flow measure of nonlinear dependence(Berlin ; Heidelberg : Springer, 2022) Braun, Tobias; Kraemer, K. Hauke; Marwan, NorbertCouplings in complex real-world systems are often nonlinear and scale dependent. In many cases, it is crucial to consider a multitude of interlinked variables and the strengths of their correlations to adequately fathom the dynamics of a high-dimensional nonlinear system. We propose a recurrence-based dependence measure that quantifies the relationship between multiple time series based on the predictability of their joint evolution. The statistical analysis of recurrence plots (RPs) is a powerful framework in nonlinear time series analysis that has proven to be effective in addressing many fundamental problems, e.g., regime shift detection and identification of couplings. The recurrence flow through an RP exploits artifacts in the formation of diagonal lines, a structure in RPs that reflects periods of predictable dynamics. Using time-delayed variables of a deterministic uni-/multivariate system, lagged dependencies with potentially many time scales can be captured by the recurrence flow measure. Given an RP, no parameters are required for its computation. We showcase the scope of the method for quantifying lagged nonlinear correlations and put a focus on the delay selection problem in time-delay embedding which is often used for attractor reconstruction. The recurrence flow measure of dependence helps to identify non-uniform delays and appears as a promising foundation for a recurrence-based state space reconstruction algorithm.