“Climate scientists don’t usually propose anything specific to ‘tackle climate change’ other than, well, doing something. Because according to them nothing is being done, or at least nothing was being done until very recently.
(Apparently, in climate scientists’ minds the $4 trillion invested in renewable electricity between 2000 and 2016, and hundreds of billions invested in non-electric renewable energy, count as nothing).” click here for more
Nicola Scafetta, Aberto Mirandola, Antonio Bianchini. Natural climate variability, part 2: Interpretation of the post 2000 temperature standstill International Journal of Heat and Technology. Vol. 35, Special Issue 1, September 2017, pp. S18-S26 DOI: 10.18280/ijht.35Sp0103
The period from 2000 to 2016 shows a modest warming trend that the advocates of the anthropogenic global warming theory have labeled as the “pause” or “hiatus.” These labels were chosen to indicate that the observed temperature standstill period results from an unforced internal fluctuation of the climate (e.g. by heat uptake of the deep ocean) that the computer climate models are claimed to occasionally reproduce without contradicting the anthropogenic global warming theory (AGWT) paradigm. In part 1 of this work, it was shown that the statistical analysis rejects such labels with a 95% confidence because the standstill period has lasted more than the 15 year period limit provided by the AGWT advocates themselves. Anyhow, the strong warming peak observed in 2015-2016, the “hottest year on record,” gave the impression that the temperature standstill stopped in 2014. Herein, the authors show that such a temperature peak is unrelated to anthropogenic forcing: it simply emerged from the natural fast fluctuations of the climate associated to the El Niño–Southern Oscillation (ENSO) phenomenon. By removing the ENSO signature, the authors show that the temperature trend from 2000 to 2016 clearly diverges from the general circulation model (GCM) simulations. Thus, the GCMs models used to support the AGWT are very likely flawed. By contrast, the semi-empirical climate models proposed in 2011 and 2013 by Scafetta, which are based on a specific set of natural climatic oscillations believed to be astronomically induced plus a significantly reduced anthropogenic contribution, agree far better with the latest observations.
Glenn A. Hodgkins, Paul H. Whitfield, Donald H. Burn, Jamie Hannaford, Benjamin Renard, Kerstin, Stahl, Anne K. Fleig, Henrik Madsen, Luis Mediero, Johanna, Korhonen, Conor Murphy, Donna Wilson. Climate-driven variability in the occurrence of major floods across North America and Europe Journal of Hydrology Volume 552, September 2017, Pages 704-717 https://doi.org/10.1016/j.jhydrol.2017.07.027
Concern over the potential impact of anthropogenic climate change on flooding has led to a proliferation of studies examining past flood trends. Many studies have analysed annual-maximum flow trends but few have quantified changes in major (25–100 year return period) floods, i.e. those that have the greatest societal impacts. Existing major-flood studies used a limited number of very large catchments affected to varying degrees by alterations such as reservoirs and urbanisation. In the current study, trends in major-flood occurrence from 1961 to 2010 and from 1931 to 2010 were assessed using a very large dataset (>1200 gauges) of diverse catchments from North America and Europe; only minimally altered catchments were used, to focus on climate-driven changes rather than changes due to catchment alterations. Trend testing of major floods was based on counting the number of exceedances of a given flood threshold within a group of gauges. Evidence for significant trends varied between groups of gauges that were defined by catchment size, location, climate, flood threshold and period of record, indicating that generalizations about flood trends across large domains or a diversity of catchment types are ungrounded. Overall, the number of significant trends in major-flood occurrence across North America and Europe was approximately the number expected due to chance alone. Changes over time in the occurrence of major floods were dominated by multidecadal variability rather than by long-term trends. There were more than three times as many significant relationships between major-flood occurrence and the Atlantic Multidecadal Oscillation than significant long-term trends.
Collin P. Ward, Sarah G. Nalven, Byron C. Crump, George W. Kling, Rose M. Cory. Photochemical alteration of organic carbon draining permafrost soils shifts microbial metabolic pathways and stimulates respiration. Nature Communications 8, Article number: 772 (2017) doi:10.1038/s41467-017-00759-2
In sunlit waters, photochemical alteration of dissolved organic carbon (DOC) impacts the microbial respiration of DOC to CO2. This coupled photochemical and biological degradation of DOC is especially critical for carbon budgets in the Arctic, where thawing permafrost soils increase opportunities for DOC oxidation to CO2 in surface waters, thereby reinforcing global warming. Here we show how and why sunlight exposure impacts microbial respiration of DOC draining permafrost soils. Sunlight significantly increases or decreases microbial respiration of DOC depending on whether photo-alteration produces or removes molecules that native microbial communities used prior to light exposure. Using high-resolution chemical and microbial approaches, we show that rates of DOC processing by microbes are likely governed by a combination of the abundance and lability of DOC exported from land to water and produced by photochemical processes, and the capacity and timescale that microbial communities have to adapt to metabolize photo-altered DOC.
“USHCN raw data is by far the best long term weather record on Earth. The bottom line is the US is cooling, and NOAA temperature adjustments are fraudulent.” click here
“The global temperature record doesn’t demonstrate an upward trend. It doesn’t demonstrate a lack of upward trend either. Temperature readings today are about 0.75°C higher than they were when measurement began in 1880, but you can’t always slap a trendline onto a graph and declare, “See? It’s rising!” Often what you think is a pattern is actually just Brownian motion. When the global temperature record is tested against a hypothesis of random drift, the data fails to rule out the hypothesis. This doesn’t mean that there isn’t an upward trend, but it does mean that the global temperature record can be explained by simply assuming a random walk. ” click here