Seattle (above) / December 8, 2016
Question from Susan: Do you imagine that Algos are the reason we are seeing an ever increasing expansion and constant change in these monstrous cloud formations all around the planet. Could be they are now able to experiment very quickly and alter the Algos again and again to find various results. Do you think this is possible? What you said about Algos applied to optical waves made me think of this.
Answer from Anonymous: Yes, definitely.
Computer controlled transmissions – with millions of possibilities.
Mapping the landscape of climate engineering
P. Oldham, B. Szerszynski, J. Stilgoe, C. Brown, B. Eacott, A. Yuille
Published 17 November 2014.DOI: 10.1098/rsta.2014.0065
In the absence of a governance framework for climate engineering technologies such as solar radiation management (SRM), the practices of scientific research and intellectual property acquisition can de facto shape the development of the field. It is therefore important to make visible emerging patterns of research and patenting, which we suggest can effectively be done using bibliometric methods. We explore the challenges in defining the boundary of climate engineering, and set out the research strategy taken in this study. A dataset of 825 scientific publications on climate engineering between 1971 and 2013 was identified, including 193 on SRM; these are analysed in terms of trends, institutions, authors and funders. For our patent dataset, we identified 143 first filings directly or indirectly related to climate engineering technologies—of which 28 were related to SRM technologies—linked to 910 family members. We analyse the main patterns discerned in patent trends, applicants and inventors. We compare our own findings with those of an earlier bibliometric study of climate engineering, and show how our method is consistent with the need for transparency and repeatability, and the need to adjust the method as the field develops. We conclude that bibliometric monitoring techniques can play an important role in the anticipatory governance of climate engineering.
This article has aimed to contribute to democratic deliberation on the governance of climate engineering in general, and SRM in particular, by seeking to make visible emerging patterns and structures in scientific research and patent activity. In doing so, we have deliberately sought to build on existing work with the aim of establishing a common baseline for monitoring climate engineering that allows for flexibility and clarity in defining climate engineering and also accommodating emerging developments through a modular approach. As the discourse of climate engineering alters, although there is no objective way to define its shifting boundary, existing modules can nevertheless be adjusted or new ones added in a transparent way in order better to capture the changing landscape.
Furthermore, we advocate transparency in making raw data available for comparative analysis by research teams to permit the stabilization of a consensus baseline over time. Finally, we have argued that greater clarity on patent activity for climate engineering could be achieved through cooperation with the World Intellectual Property Organization and the European Patent Office to facilitate the targeted identification of climate engineering activity under a variety of definitions.
Marking the edges of climate engineering as an issue, research domain or area of innovation is exceptionally challenging, given the profound scientific and definitional uncertainties. Nevertheless, we have argued that the bibliometric monitoring of research and patenting activity could constitute an important part of the anticipatory governance of climate engineering.
It is valuable because of its capacity to make visible the often-hidden networks of collaboration, funding and problem-definition involved in emerging areas of science and technology, and to provide a transparent evidence base that can inform assessment and democratic deliberation.
Climate engineering and climate tipping-point scenarios
• J. Eric Bickel author
1. 1.Graduate Program in Operations Research, Center for International Energy and Environmental PolicyThe University of Texas at AustinAustinUSA
Article First Online: 06 February 2013 / DOI: 10.1007/s10669-013-9435-8
Many scientists fear that anthropogenic emissions of greenhouse gases have set the Earth on a path of significant, possibly catastrophic, changes. This includes the possibility of exceeding particular thresholds or tipping points in the climate system. In response, governments have proposed emissions reduction targets, but no agreement has been reached. These facts have led some scientists and economists to suggest research into climate engineering. In this paper, we analyze the potential value of one climate engineering technology family, known as solar radiation management (SRM) to manage the risk of differing tipping-point scenarios. We find that adding SRM to a policy of emissions controls may be able to help manage the risk of climate tipping points and that its potential benefits are large. However, the technology does not exist and important indirect costs (e.g., change in precipitation) are not well understood. Thus, we conclude the SRM merits a serious research effort to better understand its efficiency and safety.
A technique for generating regional climate scenarios using a nearest-neighbor algorithm
 A K-nearest neighbor (K-nn) resampling scheme is presented that simulates daily weather variables, and consequently seasonal climate and spatial and temporal dependencies, at multiple stations in a given region. A strategy is introduced that uses the K-nn algorithm to produce alternative climate data sets conditioned upon hypothetical climate scenarios, e.g., warmer-drier springs, warmer-wetter winters, and so on. This technique allows for the creation of ensembles of climate scenarios that can be used in integrated assessment and water resource management models for addressing the potential impacts of climate change and climate variability. This K-nn algorithm makes use of the Mahalanobis distance as the metric for neighbor selection, as opposed to a Euclidian distance. The advantage of the Mahalanobis distance is that the variables do not have to be standardized nor is there a requirement to preassign weights to variables. The model is applied to two sets of station data in climatologically diverse areas of the United States, including the Rocky Mountains and the north central United States and is shown to reproduce synthetic series that largely preserve important cross correlations and autocorrelations. Likewise, the adapted K-nn algorithm is used to generate alternative climate scenarios based upon prescribed conditioning criteria.
 Integrated assessment (IA) studies link biophysical and socioeconomic models for studying the effects of climate change and other anthropogenic stressors on both natural and human systems [Cohen, 1986; Dowlatabadi and Morgan, 1993; Mearns et al., 1996]. They do this by predicting, for example, local patterns of spatial change in agroecosystem boundaries, soil carbon storage, changes in soil moisture, and water resource availability, and then, in conjunction with various development policies, simultaneously address the implications of these local impacts at broader regional and national scales [Rosenzweig and Parry, 1994; Smit et al., 1996; Yates and Strzepek, 1996, 1998a].
 Because of their detailed characterization of biophysical processes, many of these IA models require high-resolution climate data (e.g., precipitation, temperature, wind, solar radiation, and so on) at relatively fine spatial and temporal (at least daily) scales to drive these process models. To calibrate these models and evaluate their performance, observed meteorological data sets are used as forcing variables [Yates and Strzepek, 1998a, 1998b; Yates et al., 2000; Kenny, 2000]. To study the likely effects of climate change for IA analysis, climate scenarios are generated through downscaling techniques that involve developing statistical relationships between historic meteorological observations and outputs from regional and/or global climate models [Wilks, 1992; Robock et al., 1993; Easterling, 1999; Hewitson and Crane, 1996; Semenov, 1997; Wilby et al., 1998; Sailor and Li, 1999a, 1999b; Mearns et al., 1999]. While these approaches for simulating climate scenarios for IA analysis are useful, they do have limitations. For example, a climate change scenario based on output from a general circulation model (GCM) simulation is a single realization of many possible climatic futures, while an ensemble of climate scenarios that could rigorously explore the decision space of IA models would be more useful. Also, in many cases GCMs do not adequately replicate the historic climate of a region, so there is a great deal of uncertainty regarding the regional GCM output under future scenarios of increasing CO2 and aerosol changes.
North of Antarctica & west of Africa (above) http://go.nasa.gov/2hm44aU
West of South America & north of Antarctica
These circular ‘cutouts’ make me think of what 1PacificRedwood always
points out, like they are blowing holes in the atmosphere with their transmitters?
Above Antarctica & west of South America http://go.nasa.gov/2gXyEL2
Off the coast of Baja CA (two above) / Dec.7, 2016 / ‘stringy’ lines within geometric formations http://go.nasa.gov/2hm97by http://go.nasa.gov/2hm4lL9