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Covid in Italia dal 1.o giugno al 14 dicembre 2020: decessi, clima e principio di precauzione

Franco Zavatti

December 10, 2020

Last Updated: December 22, 2020

La versione originale è pubblicata su Climatemonitor.it

Devo dire che non sono troppo convinto nell’affrontare questo argomento perché non conosco bene le dinamiche della situazione covid-19.
Sono anche sicuro di non avere intenti critici nei confronti della gestione della pandemia da parte delle autorità competenti e so con certezza che, se abbiamo autorità che gestiscono la salute, dobbiamo seguire le loro indicazioni. La critica verrà (se è il caso) successivamente, a bocce ferme.
A titolo personale, sono un soggetto a rischio e seguo pedissequamente le regole (sono anche in pensione e posso permettermi di farlo). Esco di casa la mattina alle 6 per la mia passeggiata di qualche chilometro (non per il virus, l’ho sempre fatto); rientro alle 7 ed esco solo in caso di necessità.
Non avrei intenzione di contagiarmi e attendo speranzoso il vaccino.
Qui mi propongo di mostrare, non solo l’andamento dei contagi e la possibilità (credo concreta) che l’Italia stia uscendo dalla seconda ondata, ma anche che l’uso del principio di precauzione (“verrà applicata la massima precauzione” ci è stato detto e ripetuto) sembra non rispondere ai criteri di economicità che ho descritto nel Sono scettico. Sì ma perché? scaricabile anche dalla barra destra di Climatemonitor (l’esempio estremo che avevo fatto allora era che, in base al principio di precauzione, non posso uccidere due persone per evitare che mio figlio possa [forse] ferirsi ad un dito). A partire da queste considerazioni, vorrei discutere l’uso del principio di precauzione applicato nello stesso modo (senza economicità) nella gestione dell’inesistente cambiamento climatico causato dall’uomo (che io continuo a chiamare riscaldamento globale antropogenico o AGW).


Fig.1: In alto: casi di covid-19 in Italia dal 1.o giugno al 14 dicembre 2020. La riga bordeaux è un filtro passa-basso di finestra 7 giorni e la riga verde è una gaussiana. La riga tratteggiata è il numero massimo di casi della prima ondata (21 marzo 2020, pari a 6554). Le scritte esterne, a destra, e il riquadro rosso saranno discussi successivamente. Le date in rosso sono puramente indicative e, verso la fine di novembre, sbagliate di 4-5 giorni. In basso: spettro della serie, con il riquadro giallo che mostra un ingrandimento della prima parte. sono indicati dalle frecce rosse alcuni periodi caratteristici (o che lo sono stati nel corso dell’analisi) espressi in settimane (w=settimane). La più recente versione di questa figura è disponibile qui.

Uso anche i dati di popolazione e di mortalità giornaliera di Svezia (S) e Brasile (BR) per calcolare i decessi per milione di abitanti di altre due nazioni molto citate nei (tele)giornali dei mesi passati.

Intanto vediamo la situazione dei contagi in Italia: nella figura sono riportati i casi di contagio dal primo giugno al 7 dicembre 2020 insieme ad un filtro passa-basso che serve a smussare la ben nota pratica del minor numero di tamponi nel fine settimana e del conseguente minor numero di casi.
Ho poi aggiunto anche una funzione gaussiana (non un fit) i cui parametri sono stati aggiustati per tentativi in corrispondenza del giorno 170 (circa 17-18 novembre), quando la curva era attorno al suo massimo. Da quel momento i parametri non sono stati più modificati. Le date in rosso sono state aggiunte per un’indicazione di massima del periodo.

Il grafico dei contagi ci dice che stiamo uscendo dalla seconda ondata, seguendo bene, per ora, l’andamento della gaussiana (σ=19 gg; FWHM=2.34σ=44.5 gg). In questi giorni si osserva un “rimbalzo” (un allontanamento dalla gaussiana) che spero sia momentaneo. Capisco che chi usa il principio di precauzione continui in modo martellante a sottolineare l’aspetto catastrofista (i contagi calano ma non possiamo rilassarci e dobbiamo temere la terza ondata a gennaio …) e, dal loro punto di vista, condivido.

Nella parte inferiore della figura mostro lo spettro (il riquadro giallo è un ingrandimento della parte iniziale) con indicati (frecce rosse) alcuni massimi caratteristici anche di periodi precedenti, quando i dati erano in numero inferiore. Il periodo di 1 settimana non ha la freccia perché non ha necessità di essere sottolineato: il ritmo settimanale è indiscutibile ed è il massimo spettrale principale.
Si osserva un massimo a 3 settimane (e nel corso del tempo si sono visti massimi a 2 e a 5 settimane), ma da notare sono anche quelli di metà settimana (0.5, 1.5-1.7, 2.5, 6.5 settimane), in gran parte deboli ma presenti, forse armoniche secondarie del forte picco a 0.5. Non so bene a cosa attribuire questi picchi e non tenterò spiegazioni che sarebbero del tutto casuali e non documentate. Negli aggiornamenti della figura 1 gran parte di questi picchi di metà settimana scompaiono e compaiono alternativamente.
La parte superiore della figura mostra scritte in alto a destra, esterne, che elencano il numero di abitanti delle cinque nazioni prese in considerazione e i rapporti tra il numero di abitanti in USA, Gran Bretagna, Italia, Svezia, Brasile; ho aggiunto anche l’Iran (IR), nella figura aggiornata, dopo aver ascoltato al TG2 che è una delle due nazioni con una situazione peggiore di quella italiana ma ho potuto verificare che in realtà è nettamente migliore della nostra: 653 decessi /milione al 19/12). C’è poi anche un riquadro a sinistra che presenta il numero complessivo, nelle cinque nazioni, e al 14 dicembre 2020, di decessi per covid-19. Si legge, rispettivamente, 308033, 64402, 65011, 281775 e 327501, numeri che si traducono in 806, 965, 1078, 737 e 868 decessi per milione di abitanti.
I decessi complessivi per i quattro stati, rapportati alla popolazione USA, diventano, tramite il rapporto a destra nel grafico, 316, 351, 282 e 327 mila, tutti superiori al numero dei decessi americani. Proprio oggi ho ascoltato al telegiornale (8 dicembre 2020, tg2 delle 20:30) la notizia che l’Italia è il primo paese al mondo per numero di decessi, seguita dalla Spagna.


Dal confronto con gli altri stati deduco che l’uso del principio di precauzione non ha cambiato in modo significativo la situazione dei contagi covid-19, anzi sembra proprio che ci si trovi peggio che in altre nazioni “meno virtuose” o supposte tali. A “conforto” di questa affermazione riporto anche i dati della Spagna: 46.94 milioni di abitanti; USA/ES=8.14; 49596 decessi al 16/12/2020, con circa 395 mila decessi complessivi (1035 per milione), numero, normalizzato alla popolazione, nettamente inferiore agli altri quattro e paragonabile a quello dell’Italia.
Se la notizia del tg2 è vera (un veloce passaggio dai giornali on-line non ha dato esiti), non ci sono situazioni peggiori di quella italiana con cui confrontarsi. Il giorno 19/12 ho ascoltato la notizia che ci sono due nazioni peggiori dell’Italia e una è l’Iran: come ho specificato più in alto, a me la situazione iraniana risulta migliore di quella italiana. Riassumo il confronto dei decessi per milione di abitanti tra le sei nazioni che ho considerato nella tabella successiva:

CountryDeaths/million
Italy1135
U.K.1015
Brazil890
U.S.A.846
Sweden784
Iran651

Il clima
E vengo al clima: il principio di precauzione, di massima precauzione, viene usato nel contrasto (assunto come possibile, anzi sicuramente attuabile, ma mai dimostrato) al cambiamento climatico -sempre e solo provocato dalle attività umane e sempre riferendosi all’ambiente e quasi mai al clima- senza prima aver analizzato il vantaggio economico dell’azione e basandosi sulla supposizione di una minaccia assolutamente senza uguali (quella climatica è una minaccia identica alla perdita di vite umane nel caso del covid-19) che deve essere evitata, costi quel che costi.
C’è qualcosa che allo stato delle cose dimostri che l’uso del principio di precauzione possa dare, nel caso climatico, risultati migliori di quelli forniti in ambito sanitario? Io credo di no e, in ogni caso, è necessario che qualcuno dimostri che il miglioramento è dovuto all’uso di questo principio. Ma il risultato finale sarà (come nel caso del covid) una perdita di capacità economica e di ricchezza, che dovrà essere sostenuta dai soldi (quali?) dei contribuenti, con, in cambio, nessun miglioramento della situazione climatica dovuto al “contrasto al clima”, proprio come non si è visto nessun miglioramento nel numero dei decessi rispetto agli altri paesi.

L’uso del principio di precauzione “a prescindere” può risultare molto pericoloso e può produrre danni che si protrarranno a lungo e che pagheranno le generazioni future.

Dati e grafico aggiornati nel sito di supporto

The 4.2 Ka event in some series, from Norway to Southern Italy

Franco Zavatti

November 26, 2020

Last updated: November 27, 2020

Paleo-climatic series show a quick climatic event happened about 4200 years ago (4.2 kyr BP or Ka), i.e. around 2200 BCE (hereafter 4.2-event), seen as different from place to place (Cartier et al., 2019), more often as arid, to which have been attributed important social and political changes have been attributed, such as the end of: Akkadic Empire; Ancient Egyptian Kingdom; Lingshu culture in China, or the beginning of the end of the Indus Valley civilization due to drought, also if there is no consensus in considering it a global and well time-defined event.
I will follow here a paper by Cartier et al. (2019), about the 4.2-event detected in Lake Petit (French Alps, north of Nice) sediments, where observed δ18O data (figure 1) are available, in the aim to compare the existence of the event also in other series from two lakes, in Norway and Italy, and from a cave in Portugal.

Fig.1: Lake Petit (44°06′789″N; 7°11′342″E; 2200m s.l.m.). Oxygen isotopic series from 0-5 ka old diathomees. The cyan vertical line marks the (time) position of the 4.2-event. The acronym VSMOW refers to a pure-water standard isotopic ratio, the values of δ18O have been computed with respect to.


The rise of Oxygen isotopic ratio (a proxy for temperature) around 4 Ka is the signature of the 4.2-event, (Cartier et al., 2019), in that supported also by the variations of other climatic and environmental parameters in the same period. For example, we observe, in the lower section of <a href=”http://www.zafzaf.it/clima/cm157/fig2-cartier15.png&#8221; target=_blank>figure 2</a> of another paper from the same author (Cartier et al., 2015, again about the lake Petit), that, shortly after 4 Ka, strong and quick fluctuations in diatom abundance appear.
As above outlined, comparison with other situations, also at different latitudes, should bring to some hypothesis about a general behavior, or not, at least in the European framework.
The “Lago Grande di Monticchio” (Potenza, Southern Italy)
In 2015 I published analysis and data, referred to sediments of 5 lakes that, for the Monticchio Large Lake (a little one also exists), covered a 100 Kyr period. Here, in what follows, I use the same series in the range 0-10 Ka, looking for some jump or variation around 4 Ka.

Fig.2: Monticchio Large lake (40.93333 N; 15.5833 E; 656 m a.s.l.) and 5 of the available biological species and magnetic susceptibility between 0 and 10 Ka. The vertical dashed pink line defines the 4.2-event age.

All the series show a peculiar situation around 4.2 Ka, except dry-product density, apparently almost insensible to the change. Pollen count and &delta;<sup>18</sup>O, after the 4.2-event, remain stable for 600-800 years and then again begin to oscillate as in the nearer-us period, mainly the Oxygen isotopic ratio; this is a behavior to be outlined too, as a sign of change. Pollen count slightly drops, with values among the lowest ones in the period, and that can point to some problem linked to the arid period.
Abundance of wood taxa (caption) between 0 and 10 Ka shows a change too after 4.2-event, in the sense that higher frequency oscillations, well visible during the former period, do fade. Herbaceous taxa (not shown) have a complementary shape, in the sense that wood limits the prairies and vice-versa.

The coastal lake Heimerdalsvatnet, Lofoten Islands (Norway)
Described in the paper by Balascio et al. (2011). Authors note an important variation of the Si/Ti (Silicon/Titanium) ratio, i.e. a signature of a diatom productivity grow, between 5 and 6 Ka (out of the actual range of interest) while a growing rate of the ratio has been observed before that period.


Fig.3: Heimerdalsvatnet lake (68°17.78′N, 13°39.38′E; 5 m s.l.m.). See also the TIC & TOC series, between 3 and 5 Ka, and the caption.


Sulfur (S) appears very little interested in the 4.2-event: it is a marker of marine influence and has growing values starting from 4.8 up to 7-8 Ka (Balascio et al, 2011, figure 8).
Figure 8 has been edited by adding a 4.2 Ka mark (red line). So, Si/Ti ratio shows a rapid variation just after that date while Ca and S are less sensible to the 4.2-event. Remaining elements present relevant and extended variations of limited duration after 150-200 years. Magnetic susceptibility, in spite of a similar behavior (i.e. a maximum after 150-200 years), needs a separate reasoning because its values are given only in function of the sediment depth, not in form of a time series. In order to derive the depth-to-age transformation I used the calibration curve plotted in the last frame of figure 3 (blue-red plot), computed by myself and so with possible further errors.

The cave near Alvados (Portugal)

The cave named Buraca Gloriosa is located in central-west Portugal, near to the city of Alvados, at about 30 km (~19 miles) from Atlantic Ocean. It is a little cave, 35 m (~32 yard) long, a descend E’ una piccola grotta, lunga circa 35 metri, in practice a descending corridor, with a little entrance (half a squared meter) in its higher zone. Six stalagmites have been observed and three of them in the cave floor and three at an height of 4 meters. Here I use data from a work by Thatcher et al. (2020) quoting in the references previous analyses. In their figure 7 (only a section shown here), the authors give general climate of the cave area and show arid conditions for the period 3.7-4.4 Ka. For sake of comparison, conditions for 1.4-3.3 Ka are arid-variable and the ones for 5.3-7.5 Ka wet-variable.

Fig.4: Cave near Alvados, Portugal (39°32′N, 08°47′W; 420 m a.s.l.). Uranium and Thorium isotopic ratios in the 3-5 Ka range. To be noted the different shapes of Uranium and Thorium.

Out of the five series used here, the Uranium ones (U238 e δU234) show an extreme event after 150 and 250 years the 4.2-event, while the three Thorium series are event-centered. In the Th/U, around 4.4 Ka, a relative minimum can be observed which corresponds to the strong Uranium growth.
Also for the present situation, we can say not all the elements respond to a climatic event in the same way or with the same speed.

Concluding remarks
From biological and chemical series shown here, I assume I can say that the 4.2-event has been without any doubt present, at least in Europe, also if with a variety of characters, duration and time scales.

I’m not able to define any latitudinal variation.

References
  • Nicholas L. Balascio, Zhaohui Zhang, Raymond S. Bradley, Bianca Perren, Svein Olaf Dahl, Jostein Bakke: A multi-proxy approach to assessing isolation basin stratigraphy from the Lofoten Islands, Norway, Quaternary Research, 75, 288-300, 2011. https://doi.org/10.1016/j.yqres.2010.08.012
  • Rosine Cartier, Elodie Brisset, Christine Paillès, Frédéric Guiter, Florence Sylvestre, Florence Ruaudel, Edward J Anthony and Cécile Miramont: 5000 years of lacustrine ecosystem changes from Lake Petit (Southern Alps, 2200 m a.s.l.): Regime shift and resilience of algal communities, The Holocene, 25 (8), 1231-1245, 2015. https://doi.org/10.1177/0959683615580862
  • Rosine Cartier, Florence Sylvestre, Christine Paillès, Corinne Sonzogni, Martine Couapel, Anne Alexandre, Jean-Charles Mazur, Elodie Brisset, Cécile Miramont and Frédéric Guiter: Diatom-oxygen isotope record from high-altitude Lake Petit (2200 m a.s.l.) in the Mediterranean Alps: shedding light on a climatic pulse at 4.2 ka, Clim.Past, 15, 253-263, 2019. https://doi.org/10.5194/cp-15-253-2019
  • Diana L Thatcher, Alan D Wanamaker, Rhawn F Denniston, Yemane Asmerom, Victor J Polyak, Daniel Fullick, Caroline C Ummenhofer, David P Gillikin and Jonathan A Haws. Hydroclimate variability from western Iberia (Portugal) during the Holocene: Insights from a composite stalagmite isotope record. The Holocene, 30 (7), 966-981, 2020. https://doi.org/10.1177/0959683615580862
  • Rosine Cartier, Florence Sylvestre,Christine Paillès, Corinne Sonzogni, Martine Couapel, Anne Alexandre,Jean-Charles Mazur, Elodie Brisset, Cécile Miramont and Frédéric Guiter:Diatom-oxygen isotope record from high-altitude Lake Petit (2200 m a.s.l.) in the Mediterranean Alps: shedding light on a climatic pulse at 4.2 ka, Clim.Past, 15,253-263, 2019. https://doi.org/10.5194/cp-15-253-2019
  • Data and plots available at support site

    Some chemical elements from Lake Lungo (Rieti) sediments

    Franco Zavatti

    November 25, 2020.

    Last Updated: November 26, 2020

    Scott Mensing, in collaboration with almost two italian research teams (Tor Vergata University, Rome and Tuscia University, Viterbo, but also INGV, Rome), from about 5 years (as far as I know) publishes papers derived from cores (sediments) of lake Lungo (Rieti, Central Italy), one of the actual remnants of a wide flood plain of the river Velino (lat: 42.4762; lon: 12.8470).

    Fig.1: Mappa della zona circostante il lago Lungo (da Mensing et al., 2016).
    This area has been dwelled, with wide variations, until the first Iron Age (~3100 years ago or 1100 BCE) so it has been submitted to strong human interactions manly interested to recovery of tillable zones and hill grass areas. The authors of the papers (Mensing et al., 2015; 2016; 2018; 2019) have then correctly defined their results into an hystorical-economical-climatic context which gives an overview of the specific region and also can be extended to a wide area of Central Italy. I don’t know technical methods user in core extraction, so only downloaded their series of chemical elements from PANGAEA and the magnetic suscettibility (χ), again from PANGAEA (another link) analyzed a subset of the data (8 elements and χ), in the aim to understand their behavior with respect the climatic periods covered by the 2700 years of the series (-700, +2000 CE), to define the existence of common spectral maxima and their possible association with known causes. For anyone of the 9 analysed, in the support site are available,
    Fig.2: upper plot: Titanium abundance, in counts per second (cps), and its parabolic fit (red line). This series, in the range 600-200 CE, has been used by Mensing et al., 2018, figure 4, as representative of lacustrine sediments, the only one among the chemical elements to be compared with pasture lands, wood taxa, drought index PDSI and alpine speleothemes temperature. We can derive, from that figure, a similarity between temperature and Titanium abundance. bottom plot: Lomb spectrum.

    Plot of the detrended series, showing oscillations without the underlying structure, as in figure 3

    Fig.3: Titanium series from which the mean trend has been subtracted, so to outline internal oscillations. Colored bands show 4 climatic periods: RWP= Roman Warm Period; MWP= Medieval Warm Period, LIA= Little Ice Age; MP= Modern Period.

    e the 600-2000 CE plot, in a way similar (but rolled 90°) to the one in Mensing et al., 2018, figure 4, again for Titanium.

    Fig.4: Titanium series as in Mensing et al., 2018, in the 600-2000 CE range and with the time arrow reversed with respect to above figures. Counts are divided by 1000. Its shape is similar to the Silicium, Potassium, Copper and Iron ones and all them reflect wider oscillations along the LIA, while Copper shows little variations during the transition between 600-950 CE and MWP. The two arrows mark Charlemagne birth and death years, the period the starting core of the Carolingian Kingdom (from 800 CE it became Sacred Roman Empire) has been set within.

    From single plots it is not so easy to compare both series and spectra, so I have collected these elements in two summary frames -too much dense, regrettably- allowing direct comparisons. My opinion is the <i>pdf</i> versions are better readable so a link to the latter can be found in the captions.

    Fig.5: Time serie of 8 chemical elements and magnetic susceptibility in the range 600-2000 CE. Some common shapes and similar characters can be noted, e.g. between Titanium and Potassium or the rise after 850 CE in 6 series out of 9. Arrows mark Charlemagne’s birth and death date. (pdf)

    In the figure we can observe similarities and differences, variability linked to climatic periods and almost complete indifference to them; in summary, a wide spread from which we perhaps can derive which chemical elements may be better linked to climate (proxies). For example, Titanium appears (from Mensing et al, 2018, figure 4) well correlated to Alps temperature and a little less to PDSI, Morocco and NAO, Scotland; very well correlated to the development of prairies which, in turn, are negatively correlated to the presence of wood taxa, as expected.

    Comparison with other chemical elements can lead to closer relationships with environmental and historical behaviors.

    Fig.6: Outline of Lomb spectrum of the used series. pdf. A general, similar character appears in the spectra, apart Cobalt where high frequency peaks contain more power tha the low-frequency ones. The spectral maxima distribution over variable amplitude classes can be observed in this histogram and in its caption.

    Spectra too, show a lot of similarity: in detail, maxima between 1000 and 1500 years (6 events), 250 and 300 years (7), 100 and 150 years (16), 60 and 70 years (5): the first group could be linked to Eddy’s solar cycle (1000 yrs) and to the frequency of Bond events (1470±500 yrs), while I do not know events linked to the second group. The third group could include a 104 yrs, unnamed solar cycle and the Jose cycle (~150 yrs) and the fourth one can be referred to planet’s proper -oceanic and atmospheric- frequencies. El Nino like periods (2-9 yrs) appear in a (very) low number and power within the spectral ensemble, but really they are almost all due to the Cobalt spectrum, the only one where short-period maxima are favored and that looks different from the other spectra.

    Concluding remarks
    The use of the chemical elements alone allows to define some hypothesis (solar, El Nino, oceanic influence, sensibility or not to large scale climatic variability) but, in order to analyse historical, social and climatic of the region will be necessary a comparison with other elements, as correctly the authors have realized in their 2018 and 2119 papers, by means of the use of the complex relationship among recovery of tillable soil, prairie-wood rotation, military operations, region’s social organization. It is interesting, too, to study such relationships in a frontier territory that, at least in the High Middle Ages, was other than a quiet place (in spite of the Farfa Abbey control) and the site of important -also cultural- changes.

    A little disappointment: the authors use here the term “Athropocene”, a non-existent definition from a geological point of view (the only one that matters). It must be considered only a political product used to confirm a negative concept toward the humans. This wrong idea, in my opinion, has no-motif to exist because we are here and can think and known only thank to the human evolution so far.
    Update: After this text has been written, I analysed all the available elements but Molibdenum. Results are available at the support site, highlighted with orchid and similar colors.

    References

  • Edward M. Schoolman, Scott Mensing, Gianluca Piovesan: Land Use and the Human Impact on the Environment in Medieval Italy ,Journal of Interdisciplinary History, XLIX:3, 419-444, 2019.https://doi.org/10.1162/jinh_a_01303
  • Mensing Scott, Schoolman Edward M, Tunno Irene, Noble Paula, Sagnotti Leonardo, Florindo Fabio: Historical ecology reveals landscape transformation coincident with cultural development in central Italy since the Roman Period.,Scientific Reports, 8, 2138, 2018. https://doi.org/10.1038/s41598-018-20286-4
  • Mensing Scott, Tunno Irene, Cifani Gabriele, Passigli Susanna, Noble Paula, Archer Claire, Piovesan Gianluca: Human and climatically induced environmental change in the Mediterranean during the Medieval Climate Anomaly and Little Ice Age: A case from central Italy.,Anthropocene, 15, 49-59, 2016. https://doi.org/10.1016/j.ancene.2016.01.003
  • Mensing Scott, Tunno Irene, Sagnotti Leonardo, Florindo Fabio, Noble Paula, Archer Claire, Zimmerman Susan, Pavon-Carrasco Francisco Javier, Cifani Gabriele, Passigli Susanna, Piovesan Gianluca: 2700 years of Mediterranean environmental change in central Italy: a synthesis of sedimentary and cultural records to interpret past impacts of climate on society. Quaternary Science Reviews, 116, 72-94, 2015. https://doi.org/10.1016/j.quascirev.2015.03.022

  • Data and plots are available at the support site

    Some Chemical Elements from Lake Lungo Sediments (Rieti, Central Italy)

    Scott Mensing, in collaboration with at least two italian research teams (Tor Vergata University, Rome and Tuscia University, Viterbo, but also with INGV Rome), publish papers derived by cores (sediments) from Lake Lungo, one of the actual residuals of a wide flooding plains of the river Velino (lat: 42.4762; lon: 12.8470)

    Fig.1: Map of Lake Lungo and its surroundings(from Mensing et al., 2016).

    Peoples occupied this region beginning from the Iron Age (~3100 years ago or ~1100 BCE) so it underwent strong interactions s, very interested in the recovery of wet lands by means of drainage and of hill grass. Authors of tsing et al., 2015;


    Questa zona è stata abitata, con oscillazioni a volte molto ampie, fin
    dalla prima età del ferro (~3100 anni fa o 1100 BCE) ed è stata quindi soggetta a forti
    interazioni con l’uomo, particolarmente interessato al recupero di terre
    coltivabili tramite drenaggio delle zone umide e paludose del fondo valle e
    ai pascoli collinari. Gli autori della serie di lavori (Mensing et al., 2015;
    2016; 2018; 2019) hanno quindi correttamente inquadrato i loro risultati in
    un contesto storico-economico-climatico che fornisce una visione della zona
    specifica (ma che può anche essere estesa ad un’ampia parte dell’Italia
    Centrale).

    Non conoscendo i metodi usati nei carotaggi, mi sono limitato a
    scaricare le loro serie di elementi chimici (da PANGAEA) e la serie di suscettibilità magnetica
    (χ, sempre da PANGAEA, ad un link diverso) e ad analizzare una parte di
    questi dati (8 elementi chimici e χ), cercando di capire il loro
    comportamento rispetto ai periodi climatici presenti nei 2700 anni coperti
    dalle serie (-700, +2000 CE), di definire la presenza di massimi spettrali
    comuni e la loro eventual associazione con cause note.

    Per ognuna delle 9 serie analizzate sono disponibili nel sito di
    supporto,
    il grafico e lo spettro Lomb, come in figura 2 per il Titanio

    Fig.2: grafico superiore: Abbondanza del Titanio,in conteggi al secondo (cps), e il suo fit parabolico (linea rossa). La serie, nell’intervallo 600-200 CE, è stata usata in Mensing et al., 2018, figura 4, come rappresentativa dei sedimenti lacustri l’unico tra tutti gli elementi chimici ad essere confrontato con i pascoli, le specie boschive, l’indice di siccità (PDSI), la temperatura da speleotemi delle Alpi. Da quella figura si può dedurre una similitudine tra temperatura e abbondanza di Titanio.
    grafico inferiore: spettro Lomb.


    2016; 2018; 2019) hanno quindi correttamente inquadrato i loro risultati in
    un contesto storico-economico-climatico che fornisce una visione della zona
    specifica (ma che può anche essere estesa ad un’ampia parte dell’Italia
    Centrale).

    Non conoscendo i metodi usati nei carotaggi, mi sono limitato a
    scaricare le loro serie di elementi chimici (da PANGAEA) e la serie di suscettibilità magnetica
    (χ, sempre da PANGAEA, ad un link diverso) e ad analizzare una parte di
    questi dati (8 elementi chimici e χ), cercando di capire il loro
    comportamento rispetto ai periodi climatici presenti nei 2700 anni coperti
    dalle serie (-700, +2000 CE), di definire la presenza di massimi spettrali
    comuni e la loro eventual associazione con cause note.

    Per ognuna delle 9 serie analizzate sono disponibili nel sito di
    supporto,
    il grafico e lo spettro Lomb, come in figura 2 per il Titanio

    Fig.2: grafico superiore: Abbondanza del Titanio,in conteggi al secondo (cps), e il suo fit parabolico (linea rossa). La serie, nell’intervallo 600-200 CE, è stata usata in Mensing et al., 2018, figura 4, come rappresentativa dei sedimenti lacustri l’unico tra tutti gli elementi chimici ad essere confrontato con i pascoli, le specie boschive, l’indice di siccità (PDSI), la temperatura da speleotemi delle Alpi. Da quella figura si può dedurre una similitudine tra temperatura e abbondanza di Titanio.
    grafico inferiore: spettro Lomb.

    Global Temperature: Models and Observations

    Luigi Mariani and Franco Zavatti
    December 08, 2019

    IPCC AR5 (WG1) report figure 11.25a, reproduced below as figure 1 is really important. The report doesn’t refer to the author’s name but, from the Acknowledgements at the bottom of the chapter, a sentence restricts the research to a couple of people: “The authors thank Ed Hawkins (U.Reading, UK) for his work on key sinthesis figures and Jan Sedlacek (ETH,Switzerland) for his outstanding work on the production of numerous figures”.

    Fig.1:Figure 11.25 of AR5. Forecasts through 2050 from more than 100 GCMs are shown, along with all the Representative Concentration Pathway (RCP) and observations. Upper and lower envelopes in figure 2 have been derived from this plot and HadCRUT4 Land-Ocean global temperature comes from the Climate Research Unit (CRU).

    In more details, figure 11.25a shows global temperature anomaly from 1986 through 2050, with respect to the average temperature within the same period (data has been derived from 4 global temperature datasets including HadCRUT4), for the following situations:

    1. 41 “historical” runs, i.e. referred to 1986-2005 (gray lines).
    2. 137 forcasting runs AOGCM models, plotted with colors defined by the RCPs, from the one with less CO2 (RCP 2.6) through the one with more CO2 (RCP 8.5).
    3. Global temperature from 4 observed series (1986-2012, black thick line), i.e. HadCrut4 (Hadley Center/Climate Research Unit gridded surface temperature data set, Morice et al., 2012); Interim reanalysis of air global surface temperature (ERA-Interim) from European Centre for Medium Range Weather Forescast (ECMWF), Simmons et al, 2010; GISTEMP dataset (Goddard Institute of Space Studies Surface Temperature Analysis), Hansen et al., 2010 and NOAA analysis, Smith et al., 2008.

    We are not alone to comment that plot, as it appears from the post “Comparing CMIP5 & observations” at https://www.climate-lab-book.ac.uk/comparing-cmip5-observations/ (hereafter Climate-lab) where observed thermal trends 2013-2019, have been added. In the comments that follow the post, Ed Hawkin offers useful details and the 5-and-95% lines referred to the runs of the whole set of 137 models. Also based on these information we produced the plot in figure 2 showing the essentials traits of figure 11.25a, upper and lower envelope of the 100+ models, compared to the HadCRUT4 anomaly (hereafter HC4).

    Fig.2: Some summary lines from figure 11.25 of IPCC AR5. Upper and lower envelope and their median compared to HC4, updated to 2019.

    From the last figure, it appears that

    • Upper envelope scarcely reproduces real data (and we can see in figure 1 as they are represented by the individual model runs). Observed data constantly set themselves within the space between the lower envelope (lower red line) and the median (gray central line).
    • Lower envelope effectively represents the pause (or hiatus) between 2001 and 2013, the period when global temperature did not grow within the two strong El Nino 1997-1998 and 2015-2016 (it is known that GCMs cannot describe such events, due to the lack of theoretical basis).

    That appears to be a new information: models are not able to describe the 2001-2013 pause, while their lower envelope can do that. Why?

    If we scale by -0.2°C the HC4 series, obtain figure 3 which shows in greater detail as the lower envelope can reproduce the temperature pause.

    Fig.3: As figure 2, with only the envelopes (upper, Tn, red; lower, Tx, pink) and HC4 scaled by -0.2°C with respect to figure 2.

    After such a new fact has been defined, we need to understand if the envelopes have some sense and if their existence can be associated to some physical, real character. Envelopes are the extreme values, both maximum and minimum, of figure 1, irrespective of the model that gave the extrema.

    Minimum value of any model depends on RCPs; on the way to manage specific and less known aspects of the climatic engine and on the set of starting parameters and their tuning (we can remember here a test produced by NCAR/UCAR in 2016, described e.g. in Judith Curry’s blog, where 30 variations of the global atmospheric temperature, each one less than a trillionth of degree gave, in the model outputs, 30 very different outputs).

    To compare observations and single models, we use here three models from KNMI,
    namely BNU-ESM and CSSM4 for RCP 8.5 e 2.6 compared to HC4,

    Fig.4:Two models, anyone computed for two RCPs, compared to HC4 (blue line)

    or the ACCESS 1.3 model with RCP 4.5, compared to HC4 only for the period of observations.

    Fig.5:ACCESS1.3 model, RCP 4.5, compared to HC4 (blue line).

    Comparison with observations doesn’t give a good hope for high quality forecasts and also the model in figure 5 that, as a whole, appears to be comparable to observations, shows noticeable differences and, from 2000, the start of the same divergence already seen in other GCMs and the lack of the hiatus.
    On the other hand, lower envelope (figure 3) can correctly reproduce observed data. But the complex of: parameters, handling of specific aspects, RCPs in which way can manage and justify the reliability of the envelope?

    In the meantime we can note that if it is true minimum of any model depends on several factors, they hardly derive from higher-valued RCPs (this case is theoretically admissible but in a very little number of cases, so that they cannot modify the general shape of the envelope). So, we obtain the best computed series with the lower values of RCP and then with forecasts through 2100 without any catastrophic behaviour (in figure 3 data are only through 2050 but nothing leads us to a sudden growth in the next 50 years).

    In order to test the models skill over the whole time extension of HC4 we computed the minimum of four models from KNMI (ACCESS1.3, BNU-ESM, CSSM4 and INMCM4) e compared it to the envelopes in figure 2 and to HC4 (when needed, series have been moved up and down by an arbitrary amount, so that the best coincidence could be attained). Result in figure 6, where we can see the ensemble of model lower values and the lower envelope describe fairly well the observations.

    Fig.6: Comparison among envelopes in figure 3, HC4 and the minimum of 4 models. Tx and HC4 are moved as labelled in the plot.

    Concluding remarks
    • Models appear to describe in the better way observed data between 1986 and 2018 when their lower envelope is used, also if the significance of such an envelope is not fully clear.

    • Lower envelope, unique instance among all the situations we could have verified, can represent also the hiatus, that is the lack of growth in global temperature between 2001 and 2013 (before El Nino 2015-16 could overcome the normal temperature run)

    • We cannot explain why the lower envelope gives the best performance with respect to observed data. We offer this behaviour of the climate analysis as a contribution to possible, further discussion.

    References

    • Smith, T.M., R.W. Reynolds, T.C. Peterson, and J. Lawrimore: Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880-2006), J. Clim., 21, 2283-2296, 2008.
    • Simmons, A.J., K.M. Willett, P.D. Jones, P.W. Thorne, and D.P. Dee: Low frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets. J. Geophys. Res. Atmos., 115, D01110, 2010.
    • Morice, C.P., J.J. Kennedy, N.A. Rayner, and P.D. Jones: Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set., J. Geophys. Res. Atmos., 117, D08101, 2012.
    • Hansen, J., R. Ruedy, M. Sato, and K. Lo: Global surface temperature change, Rev. Geophys., 48, Rg4004, 2010.
    • See also: WG1AR5_Chapter09_FINAL.pdf
      Data for this post is available at its support site.

    Thyphoons in Japan

    Franco Zavatti
    November 07, 2019. Updated: November 08, 2019

    JMA (Japan Meteorological Agency) produces and updates a series of thyphoons (in Japanese; in the English version of their site I cannot find the page) formed in the japanese area from 1951 through September 2019. Data is given as number of events in single month and as annual sum.
    The histogram of annual number of thyphoons can be seen in figure 1 along with its linear fit showing a negative slope.

    Fig.1: Annual number of thyphoons in Japan. Red line is the linear fit showing their slightly lowering frequency; its slope is (-0.4±0.3) events/decade.

    In this case too, as in other examples the links in the support site refers to, in spite of the global temperature climbing and its supposed (but actually assumed as truth by the majority of peoples) dependence on CO2 concentration and, in practice, on anthropic activity, extreme events don’t show any raise.

    Data shows wide fluctuations that, after applying a filter, presents a shape as in the upper frame of figure 2 (the shape depends on the filter window and gives only a rough suggestion about thyphoon yearly variability) with a sharp oscillation Such a suggestion is what we need for a spectral analysis of the dataset (as in the lower frame). The spectrum outlines the existence of a maximum at about 27 years as the main behaviour and a suggestion for 2-7 years maxima, a possible El Nino signature.

    Fig.2: NUmber of thyphoons per year and linear fit (the same as in figure 1). The red line is a low-pass, 15 year filter. The cyclicity appears in any evidence (also if it depends in part on the choice of the window). Bottom frame: MEM spectrum. The maximun at about 27.2 years totally dominate the spectrum.

    The period of the main spectral peak is well represented by the filter red line where the period difference between the first and the second maximum from the left is 24 years (1990-1966) and the distance between the minima is 28 years (2006-1978). With a semi-period of about 13 years we can forecast 2019 is the year of another relative maximum frequency and then assume this year will be more plenty of events than the preceeding ones (on average, of the preceding +13); it will happen at the conclusion of the thyphoons season, in November,
    as it appears in figure 3.

    I strongly suspect AGW has little to do with the number of thyphoons in the Japan sea and the “pain cries” about the fate of our planet are, all told, off topic.
    As remembered above, figure 3 shows the monthly series of thyphoons number, with pair-wise months in order to avoid too confusing plots.

    Fig.3: Monthly number of thyphoons in Japan. It clearly appears that the season runs June to November, with a grow up to August-September and a next decrease of the events. In any frame the black line refers to the first of the two months and the red line to the second one.

    We can easily see January and February are low-activity months while in April something begins to move in Japan Sea; from May to August-September the maximum activity is reached and then in November it decreases, running towards the minimum in December. An noticeable behaviour of this plot is that in nome of these months a systematic growth of the activity can be found, only fluctuations around a constant average value.

    L’Oscillazione Decadale del Pacifico
    The 27-yr spectral maximum makes raise the idea that the main cycle could depends on an external forcing which, in the Pacific Ocean, cold be the Pacific Decadal Oscillation (PDO). Along with El Niño, other large scale oscillations exist (like PNA, Pacific-North Atlantic) whose interactions could have some influence on thyphoons genesis, but I assume PDO is the most significative one. So I’ll use only the last one. In the following I present two PDO series, one from 1000 through 2000, derived from proxy data and the other, 1900-2018, observed, along with their spectra.

    Fig.4: PDO 1000 through 2000. From 1900 (pink line) PDO given by Mantua is superimposed (it is the file pdo-latest-mo.txt used in figure 5).

     


    Fig.5: 1900-2018 PDO series.

    We can derive from these series a thyphoon-compatible oscillation appears in the spectrum of the “extended” PDO but not in the Mantua “short” one, apart a possible noise around 27 years (not labelled in figure 5) of no practical significance. In such conditions it is difficult to the PDO alone the frequency modulation for thyphoons and we need imagine other forcing, effective in the Japan Sea.

    In that aim, I have reconsidered two plots, previously published in Mariani
    et al., 2018, i.e.

    1. The CFD series (Cherry Flourishing Date) in Kyoto, Japan (Aono and Kazui, 2008), 800-2000 CE. It, though, doesn’t present spectral maxima near to the thyphoons’ 27-yr.
      <img src=”http://www.zafzaf.it/clima/cm134/japan800.png”>
    2. The tree ring (juniper) in Wulan, China, again 800-2000 CE. This series shows a spectral maximum (well visible but not among the most prominent peaks) at 28.5 years. But we are in China, rather away from Japan.

    In summary, thyphoons formed around Japan show a dropping-in-time frequency and a superposed 27-yr cyclical behaviour, whose cause is not clear.

    More information for this post is available at the support site.

    References

    • Yasuyuki Aono and Keiko Kazui:
      Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century, Int. J. Climatol.,28, 905-914, 2008.
      http://dx.doi.org/10.1002/joc.1594.
    • L. Mariani, G. Cola, O. Failla, D. Maghradze, F. Zavatti: Influence of Climate Cycles on Grapevine Domestication and Ancient Migrations in Eurasia, Science of the Total Environment,635, 1240-1254, 2018. doi:10.1016/j.scitotenv.2018.4.175

    Cloud Cover and Global Temperature

    Franco Zavatti
    November 06, 2019. Updated: November 07, 2019

    A recent work by O.M. Pokrovsky (2019) analyses global temperature – cloud cover relationship. As cloud cover, he makes use of ISCCP data (some further information also in Rossow and Schiffer, 1991) and global (land+ocean) temperature series HadCRUT4.

    The paper is in Russian and for me non so easy to understand also if I can read Russian (with some difficulty) and understand some word (I don’t like a lot Google translator). For any practical scope, I can say I did not read the paper, whose English abstract reads:

    Cloud Changes in the Period of Global Warming: the Results
    of the International Satellite Project

    O. M. Pokrovsky

    Russian State Hydrometeorological University, St. Petersburg

    E-mail: pokrov_06@mail.ru

    The results of analysis of climatic series of global and regional cloudiness for 1983–2009. Data were obtained in the framework of the international satellite project ISCCP. The technology of statistical time series analysis including smoothing algorithm and wavelet analysis is described. Both methods are intended for the analysis of non-stationary series. The results of the analysis show that both global and regional cloudiness show a decrease of 2–6%. The greatest decrease is observed in the tropics and over the oceans. Over land, the decrease is minimal. The correlation coefficient between the global cloud series on the one hand and the global air and ocean surface temperature series on the other hand reaches values (–0.84) — (–0.86). The coefficient
    of determination that characterizes the accuracy of the regression for the prediction of global temperature changes based on data on changes in the lower cloud, in this case is 0.316.Keywords: cloudiness, ISCCP data, climate change, global and regional scale, climate series analysis, linear and nonlinear trends, wavelet analysis.The full text in Russian is available at the support site.

    I will use here annual data of both cloud cover (also named GCC or Global Cloud Cover) discretized from the paper’s figure 1, and HadCRUT4 e NOAA-GHCN global teperatures. The time spread has been defined by GCC and ranges from 1983 through 2009 (27 years).
    In figure 1 the original plot from which I dicretized the data used here:

    Fig.1: Annual values of the global cloud cover (GCC) in percent. Error bars have not been discretized.

    In the next plot, digitized data and their LOMB spectrum are shown:

    Fig.2: Digitized GCC and its LOMB spectrum. Original values are at constant step, so I could have compute the MEM spectrum, but uncertainess in the read process gave a almost constant step, so that I preferred to use LOMB. The green line is the linear fit from which I computed the detrended series required by LOMB computation.

    Figure 2 shows some behaviours that are worth to outline:

    1. The percent cloud cover clearly dropped from 1986 through 2000 and from 2001 raised with respect to 2000 and then remained roughly constant through 2009 (end of the series).
    2. A 2001-2009 almost constant situation looks like the “pause” or “hiatus” in global temperature (this is a definition of mine, derived from http://www.climatemonitor.it/?p=36847 post, while others begins the pause in 1998).
    3. Cloud cover is an important factor for temperature regulation; roughly, wider cover implies lower temperature and viceversa. This inverse relationship will be verified in what follows.
    4. In the bottom frame of the plot it can be seen that the cover is not random and that almost two cyclicities exist with periods 8-10 and 4 years when GCC could repeat itself with similar behaviour. The group around 0.9-1.3 year seems to show an annual -and also a semi annual one of 0.5 year- variation that could link GCC to astronomical (revolution around Sun) and perhaps a hemispherical forcing.
    5. The series, extended for only 27 years, doesn’t allow for detailed analysis of the spectrum.

    I can now pair-wise compare the series and verify the similarity of their characteristics. In the following, I will show spectra and cross-correlation functions (CCF) in order to derive the concordance between the series in a more accurate way than a simple visual analysis.

    Fig.3: Comparison between GCC (inverted) and global temperature HadCRUT4 (land+ocean) on the same time range 1983-2009.

    To be noted here as the pause is the same in both plots and also that temperature raise 1983-1999 is well described by the inverted cloud cover.
    As further example, I compared in figure 4, GCC also to NOAA annual data.

    Fig.4: Comparison between GCC (inverted) and global temperature NOAA (land+ocean), same time range 1983-2009.

    Also in this case the pause coincides for both series and, again, the temperature grow is well described by cloud cover between 1983 and 1999.

    A comparison among the spectra of all the series follows:

    Fig.5: Comparison among spectra. Power of both temperatures has been multiplied by 35, so that plots can be more readable.  MEM spectra don’t show frequencies greater than 0.5 (i.e. periods less than 2 years) to avoid problems with the Nyquist frequency range that must be 0 through 0.5.

    Spectra show  the same behaviour (maxima between 7.5 and 8.5  and at 4 years and the weak peak at about 2.5 years) i.e. show data are not only similar in shape, as it appears in the above plots, but they have in common periods we could think as linked to physical parameters of both variables.

    The GCC spectrum can be compared to wavelet analysis in
    figure 5 of Pokrovsky, 2019 paper: the ~1-yr grouped periods exist over the whole time range; the 4-yr period can be observed through 1992; it becames weaker through 2001 and disappears. The spectral maximum at about 9 years in the LOMB spectrum, within the wavelet starts at 8 years and overcames 32 years, always with a power at maximum level of the wavelet scale (with a 27-yr data range I did not consider periods greater than 20 year).
    I take this comparison as a confirmation of the LOMB spectrum of figure 5.

    To terminate this multi-level analysis, I do show the CCF between cloud cover and both the global temperature series:

    Fig.6: Cross-correlation function between cloud cover and se temperature series: The zero-lag CCF, i.e. the Pearson correlation coefficient is within -0.7 and -0.8, while Pokrovsky 2019 gives a larger value (-0.84/-0.86). I suppose that the last values have been computed from the monthly series I don’t own and that allows better time resolution.

    Figure 6 shows a correlation and that doesn’t mean variables are physically related (that’s they are independent random variables), but the behaviour shown above and mainly the spectra, suggest a physical link must exist between cloud cover and global temperature.

    I think the following phrase can be used as closing label:

    Sorry, I cannot remember the tale where temperature exclusively depends on CO2. Would someone please remember it to me? Thank you so much.

    Data for this post is available at the support site.

    References

    Looking for the 400 Kyr cycle

    Franco Zavatti and Luigi Mariani
    September 25, 2019. Last Update: November 8, 2019

    Introduction
    Climate of a place or a wider area (a vineyard, a valley, a continent, the whole planet) derives from a variety of astronomical or geophysical causes (table 1) often interacting each other in a, also complex, way giving place, e.g., to well characterized positive and negative feedbacks.
    In table 1 we denoted with an asterisk factors subjected to cycles over a very wide range of periods (hours to millions years).
    As an example, the Sun presents 11-yr activity cycles and longer cycles(Suess, Eddy, Hallstatt, etc.) e Ocean circulation shows cycles like AMOC (Atlantic Meridional Overturning Circulation) that then plays a role on the surface temperature of the Atlantic Ocean by forcing typical cycles known as AMO (Atlantic Multidecadal Oscillation).
    The consciousness of the exixtence of climte cycles has a long history.
    We can quote, e.g., the Saserna, roman treaters (writers) whose work has been lost but they are quoted by Columella, argued the climate of their epoch had become milder than in the old times, so that olive trees and vineyards can live well where it would been impossible before.
    In the same way the idea of cyclical cycles, that incuded also a deluge as in the biblic narrative, is widely spread in many cultures (precolombians peoples, australian blackfellows, ecc.). They mantained in trouble our progenitors along many millenia.
    In the first half of the XIX century Joseph Fourier, during his studies on heat transfer, realized that analyses were much simpler if a function was represented as the sum of simple trigonometric functions with adequate parameters. Also, the researches by Fourier in 1824 uses, and he was the first one, the concept of greenhouse effect, which along with atmospheric and oceanic circulation is the basis of our planet’s climate.
    Astronomical and geophysical elements Fourier used, are near to climatology because the terrestrial atmosphere is an heat machine whose motions depend on the need to equilibrate the differences due to the unequal distribution of heat on planet’s surface 1.


    1 defines the climate at different scales (from a single site to the whole planet)


    Again, from the XIX century, due to geomorphological work lead between 1800 and 1900 and summarized by Louis Agassiz the idea of the presence of glacial cycles takes place, what gives rise to modern climate studies about climate cycles, converging to the theory by Milutin Milankovich about the astronomical causes of glacial eras, next proven by Cesare Emiliani (1955) within his studies on ocean floor cores.
    In brief, what Fourier, Agassiz, Milankovich and Emiliani give us has all needs to understand the spectral analisys and its usefulness.

    Actual cycles study depends on instrumental data (temperature, precipitation, global solar radiation etc.). We outline that the presence of periodicity in independent series (like speleothemes, tree rings growth, grape harvest date) is an important reinforce to their reality. In applications, it will be important to link period analisys with (also historical) documents or narrative (e.g. legends); it follows that it may be of importance to link the astronomical cycles at periods of 2000 years (Bray or Hallstatt cycles) and 1000 years (Eddy’s cycle)(Scafetta et al., 2016) with key periods in the Holocene like:

    • great postglacial optimum
    • 4200 years ago dry event
    • miceneum climate optimum
    • iron age crisis
    • roman climate optimum
    • early medieval crisis
    • medieval climate optimum
    • crisis of the little ice age (LIA)
    • modern warming phase, after the end of  LIA
    Table 1 – Astronomical factors weighting on the amount of energy at the planet’s surface.
    • earth motion around its axis (rotation)*
    • eccentricity of earth’s orbit*
    • solar activity*
    • galactic cosmic rays (GCRs)*
    • orbital effects by the other planets of solar system*
    • inclination of terrestrial axis (with consequences on inclination of
      solar rays)*
    • quasi spherical shape of the earth (with effects on inclination of solar rays)
    Geophysical factors that modulate the effects of astronomical factors

    • ocean and land distribution*
    • distance from sea*
    • ocean streams*
    • atmospheric circolation*
    • shape and position of mountain ridges*
    • soil characteristic*
    • living beings activity (flora, fauna, mankind)*

    (*) submitted to cycles over a very large range of time scales (days to millions years).
    The large variety of periods forces to select a group of spectral cycles belonging to precise and often unknown causes.
    Here we used time series covering about 5 million years and selected cycles of some hundreds of thousands years.

    Data Analysis
    At about the end of August 2019, we have had the availability of a paper by Kent et al., 2018, where the empirical evidence for the stability of the 405-kiloyear Jupiter-Venus eccentricity cycle over at hundreds of milions years (at least 215 Myr) was presented. We never had notice of such a spectral maximum before of this paper, so used the De Boer et al.(2014) dataset “Global 5 Million Year Sea Level, Temperature, and d18Osw Reconstructions” who declared that
    Persistent 400,000-year variability of Antarctic ice volume and the carbon cycle is revealed throughout the Plio-Pleistocene published in Nature Communications (2014).
    The abstract of De Boer et al.,2014 paper reads: Marine sediment records from the Oligocene and Miocene reveal clear 400,000-year climate cycles related to variations in orbital eccentricity. These cycles are also observed in the Plio-Pleistocene records of the global carbon cycle. However, they are absent from the Late Pleistocene ice-age record over the past 1.5 million years. Here we present a simulation of global ice volume over the past 5 million years with a coupled system of four three-dimensional ice-sheet models. Our simulation shows that the 400,000-year long eccentricity cycles of Antarctica vary coherently with d13C data during the Pleistocene, suggesting that they drove the long-term carbon cycle changes throughout the past 35 million years. The 400,000-year response of Antarctica was eventually suppressed by the dominant 100,000-year glacial cycles of the large ice sheets in the Northern Hemisphere.
    The DeBoer (2014) dataset (hereafter deboer2014.txt) include 5 Myr (millions years) time span at 100 yr step values of some climatic parameters (i.e. 53000 data points) and is shown in figure 1 at 10-points step, i.e. with 5300 points represented, for the benthic δ18O (an inverse proxy for temperature).

    Fig.1: The 5 Myr dataset of d18O from benthonic foraminifera, plotted at 10-point step. The bottom plot is the enlargement of the first million years of the series. To be noted the inverse y-scale, so the plot mimics the temperature, and also the presence of the 25 MIS (Marine Isotope Stages) which denote the interglacials in 1-million years (with of course the relative glacial periods). The red lines are, respectively for top and bottom plots, low pass filter at 1000 and 300 data points window (100 and 30 Kyr).

    We decided to separate deboer2014.txt into 8 datasets of 7000 data each (the last one of 4000 data). In such a way we had something resembling a wavelets ensemble which allowed to derive spectra (MEM spectra: they are at constant step) of different and adjacent time sections, covering 700 kyr (kilo years) each one.
    At the same time we computed the Lomb-Scargle periodogram (hereafter Lomb) of the whole dataset, deriving both ~400 Kyr and ~1 Myr spectral maxima as shown in figure 2

    Fig.2: Lomb periodogram, from the CRAN R suite, of the full deboer2014.txt series. The green lines define here and in figure 3 the ensemble of secondary maxima between 0.4 and 1 Myr.Dashed line is the 99% confidence level.

    We also continued with the MEM spectrum, computing the two half-dataset (i.e. 26000 data points each) spectra, as shown in figure 3.

    Fig.3: MEM spectra of the two half-dataset sections of deboer2014.txt. The green lines define here and in figure 2 the ensemble of secondary maxima between 0.4 and 1 Myr.

    The comparison between the above spectra shows that the main ~0.4 and ~1 Myr maxima remain at about the same period with a skyrocket variation of the power during the second half section (730/103 or 7X and 230/11 or 21X); also the peak at 0.24 Myr (the leftmost one in figure 2) becomes 7 times higher (50/7) during the more recent two millions years than during the first section. The other visible peaks changed their frequency (period).
    Green lines in both figure 2 and 3 define a group of secondary maxima: to be noted the 0.74 Kyr maximum (0.68 in the upper frame of figure 3) clearly visible in the Lomb spectrum of figure 2 and that confirm that the analysis of the whole dataset (figure 2) is strongly dominated by the 2.nd (more recent) section of the dataset because the 0.74 maximum clearly emerges above the little “forest” of maxima.
    In order to verify at a better accuracy the variation of frequency along the time sequence, we used the 8 sections defined above to compute the MEM spectrum. The time sequence is listed in Table 1.

    Table 1. Deboer2014.txt. Start-End Kyr BP of the 8 sections. 100 yr step
    Sec Start Kyr End Kyr Comments
    1 5300.0 4600.1 7000 values
    2 4600.0 3900.1
    3 3900.0 3200.1 min power
    4 3200.0 2500.1
    5 2500.0 1800.1
    6 1800.0 1100.1 max power
    7 1100.0 400.1
    8 400.0 0.1 4000 values
    • Spectra computed from 3500 values, from line 1500 through 4500.
    • Time spread: 700 thousand years per file (8 excluded).

    A summary of the results is in figure 4

    Fig.4: The MEM spectrum of the 8 sequential and adjacent sections defined by color and, in one case, by line shape. Here only 400 Kyr and nearby maxima have been selected. The bottom plot is an enlargement in power (y-axis) of the top one.

    Analysis of the ~0.41 Kyr spectral maximum
    We can derive from figure 4 the suggestion that the power of spectral maxima
    evolve along the sections (i.e. with time) and a more precise list of peak’s
    power confirm, as in figure 5, this hypothesis:

    Fig.5: Time evolution of the ~0.4 Myr spectral maximum power from the 8-sections
    series. The blue line is the fitting parabola of the first 6 data.

    The power variation of the 0.4 Kyr peak has nothing to do with casuality but it seems to follow a rising law (the blue line is the fitting parabola) through section 6 and then a drop not too much different from the corresponding rise. So, we can suppose that, at the end of the period 1.8-1.1 Myr (section 6), something happened, so that the power of the most important cycle in the 200-700 Kyr time lapse, begun to drop.
    We cannot know what happened before section 1 (i.e. before 5.3 Myr) but perhaps it could show a cyclic behavior with a 5.7 Myr period (4600-1100 from table 1).

    Fig.6: Time evolution of the ~0.4 Myr spectral maximum power from the 7-sections, 2-subsections each one, series. Red line-and-dot accounts for the 1.st subsection, blue line-and-dot accounts for the 2.st subsection of each section. dot-dashed lines are the respective parabolic fits.

    The characteristic shape of figure 5, relative to the 8 sections, holds again for the 14 subsections, with the data of subsections 1 appearing shifted backward by one section. We cannot explain such a behaviour, only note, as in figure 7, that a positive shift of 1 section changes notably the comparisons in figure 6

    Fig.7: Time evolution of the ~0.4 Myr spectral maximum power from the 7-sections, 2-subsections each one, series when the section of subsections 1 becomes “section+1”.

    Nothing of what has been found in the analysis of the power of the main peak of the actual series appears to be casual. It seems the result of an (unknown) evolution of external or internal forcings, spanning over millions years.

    Milankovic Cycles
    The δ18O benthic by De Boer et al. (2014), while it seems very good for 0.4-1 Myr (and more) spectral peaks, poses the problem that the 100, 41, 26 Kyr Milankovic cycles (the orbital cycles of eccentricity, obliquity and precession) cannot be derived from this series as e.g. it appears in figure 3.
    We can suppose the actual spectral maxima are too weak to be identified in the above plots, so try their “emersion” by a x1000 amplification but, as it can be seen in figure 8, a daunting result in obtained: no orbital maximum in the spectra, at all.

    Fig.8: Trying to identify the Milankovic cycles by a x1000 amplification: in the range 97-104 Kyr 6 peaks (out of 8 series) can b be identified, but nothing at all at 40 and, mainly, at 26 Kyr.


    Fig.9: MEM Spectrum of Page800 δ18O benthic 0 to 800 Kyr BP (Ka is used in place of Kyr BP). The bottom plot outlines the periods of Milankovic cycles. This plot has been already published elsewhere; here it has been slightly revised. To be noted, as a mirror of figure 7, the absence of the 400 Kyr peak.

    The data De Boer et al., 2014 used is the stacked dataset LR04 Benthic by Lisiecki and Raymo (2005) at variable step, to which a model has been applied
    in order to derive a dataset at 100 yr step. So we dowloaded the Lisiecki & Raymo’s series and computed the Lomb and wavelet spectrum. The following figures 10 and 11 show that the spectra are the same and exclude some kind of procedural error.

    Fig.10: LOMB spectrum of the Lisiecki & Raymo (2005) dataset LR04 Stack. Bottom frame is a 0-200 Kyr enlargement of the above total range. Dot-dash green lines are the 95%, white noise, confidence level


    Fig.11: Wavelet spectrum of the LR04 series computed by PAST. Due to the log2 vertical scale, the figure has been labelled with the corresponding periods in Kyr. The x-axis has been also labelled in Kyr BP.

    The last dataset also confirms that the 400 Kyr peak has low power and this is confirmed in both LOMB and wavelets

    Initial and derived data, and plots, are available at the support site

    References

    • B. de Boer, Lucas J. Lourens and Roderik S.W. van de Wal: Persistent 400,000-year variability of Antarctic ice volume and the carbon cycle is revealed throughout the Plio-Pleistocene, Nature Communications, 5, issue 2999, 2014. http://dx.doi.org/10.1038/ncomms3999
    • C. Emiliani C.: Pleistocene Temperatures, The Journal of Geology, 63, 6, 538-578, 1955. http://dx.doi.org/10.1086/626295
    • Lisiecki, L. E. and M. E. Raymo, A Pliocene- Pleistocene stack of 57 globally distributed benthic d18O records, Paleoceanography,20, PA100, 2005. http://dx.doi.org/10.1029/2004PA001071
    • Dennis V. Kent, Paul E. Olsen, Cornelia Rasmussen, Christopher Lepre, Roland Mundil, Randall B. Irmis, George E. Gehrels, Dominique Giesler, John W. Geissman and William G. Parker: Empirical evidence for stability of the 405-kiloyear Jupiter-Venus eccentricity cycle over hundreds of millions of years , PNAS, 2018.
      http://dx.doi.org/10.1073/pnas.1800891115
    • Scafetta N., Milani F., Bianchini A., Ortolani S., 2016. On the astronomical origin of the Hallstatt oscillation found in radiocarbon and climate records throughout the Holocene, Earth-Science Reviews,162, 24-43,November 2016.
      https://doi.org/10.1016/j.earscirev.2016.09.004

     

    Area concerning very warm and very cold events in the US.

    Franco Zavatti

    August 21,2019; Last Updated: November 8, 2019

    Here as United States I intend the contiguous US and use the NOAA dataset that annotates the percent area of the US invested by very warm and very cold events, from January 1895 through July 2019. The plot in figure 1 shows that the warm events are concerned with wider and wider areas and the cold events smaller and smaller areas. As it can be seen, in both cases there is a large variability, so I refer to the average behaviour derived by the linear fits.

    Fig.1: The percent area of the US interested by very warm (red) and very cold (blue, sign reversed) events. The linear fits show a rise (slope circa 0.12% per year) for the warm events and a similar decrease for the cold ones.

    All the events concerning this post are “extreme” (ie very warm and very cold) but I prefer to extract from the dataset a subset of “most extreme” events which contains only the areas wider than 40% of the US territory.
    The data, displayed in figure 2, clearly shows that the areas interested by warm events are wider tan those by cold events.

    Fig.2: The time series of the areas wider than 40%. As in fiigure 1 the cold events have the value of percent area with reversed sign.

    From the above data I extracted the decadal frequency for both warm and cold events and show the relative histograms in the next figure 3.

    Fig.3:
    The histograms of the very-very warm and very-very cold, defined as those concerning areas greater than 40%. I don’t show the last bin because of its incomplete interval (2015-2024); its value is actually 15, the same of the preceeding bin.

    The situation looks like the CEI index (extreme events in the US, see eg this post, in italian) that begins to rise from 1965 through today while, before this date, it shows a decrease (the cold activity has always decreased).
    In front of that, however, the CO2 concentration has always monothonically grown in such a way that it is difficult to associate extreme events to a growing CO2.

    All plots and data about this post are available at the post’s support site: here

    Lyon-Bron airport: precipitation and temperature

    Franco Zavatti
    August 6, 2019. Updated August 6, 2019

    In Caillouet et al., 2019 a software (SCOPE) is presented which can reproduce high resolution meteorological data. In their figures 5 and 6
    precipitation and temperature at the Lyon-Bron airport (France) have been presented as an example of the ability to generate a dataset, also in comparison with the observed data. They made available only plots of reconstructed precipitation and temperature in the range 1870-2010, so I discretized such plots and obtained, for precipitation, the series lione1.txt available in the support site and plotted in the following figure 1.

    Fig.1: 1870-2010 precipitation at Lyon-Bron airport. Also average value (red), slope of linear fit (orange) and 10-yr low-pass filter (purple) are shown. Please note as the (only two) maxima are at 1.5 times the average, to be compared to the two-fold-the-average maxima in Spain. The lowest minima (4 or 5) are only 25% of the average value.

    So, I cannot see any increasing number of extreme events; on the contrary, it seems strong rain events disappear after ca 1980. Also the lowest values on the record (4 or 5 in total) have levels of about 25% of the average value. From the overall slope it can be derived rain is decreasing along the time range considered here, at the rate of (8±2) mm per decade.

    Temperature
    As in the case of precipitation, I have discretized the “median” data of the “annual” plot in Caillouet’s figure 6 and show it in figure 2

    Fig.2: Annual temperature at Lyon-Bron airport. Also average value (red), slope of linear fit (orange) and 10-yr low-pass filter (purple) are shown.

    The temperature at the Lyon-Bron airport approximatively follows the global land temperature. If a ΔT can be derived from the low-pass filtered data, then it is less than 0.8 °C; the highest picks are some tenth of degree above the average (filtered) curve; so they cannot be defined “extreme events” and, by the way, their number is really low. Increase in intensity or frequency of so called “heat waves” cannot be inferred from the Lyon data of temperature.

    The temperature dataset displays an up and down behaviour in front of a continuously rising CO2 concentration, the same of some datasets of global land+ocean temperature (see e.g. here, in italian).

    All plots and data concernig this post are available in the support site at the author’s web server, here (gray background)

    References

    1. Laurie Caillouet, Jean-Philippe Vidal, Eric Sauquet, Benjamin Graff, and Jean-Michel Soubeyroux:
      SCOPE Climate: a 142-year daily high-resolution ensemble meteorological reconstruction dataset over France., Earth Syst. Sci. Data, 11, 241-260, 2019. https://doi.org/10.5194/essd-11-241-2019