Climate Science

2023 Climate Report: Earth’s Climate Is Fine

Posted on by Ron Clutz

Preface

This report is written for people wishing to form their own opinion on issues relating to climate. Its focus is on publicly available observational datasets, and not on the output of numerical models, although there are a few exceptions, such as Figure 42. References and data sources are listed at the end.

The observational data presented here reveal a vast number of natural variations, some of which appear in more than one series. The existence of such natural climatic variations is not always fully acknowledged, and therefore generally not considered in contemporary climate conversations. The drivers of most of these climatic variations are not yet fully understood, but should represent an important focus for climatic research in future.

In this report, meteorological and climatic observations are described according to the following overall structure: atmosphere, oceans, sea level, sea ice, snow cover, precipitation, and storms. Finally, in the last section (below), the observational evidence as at 2023 is briefly summarised.

Ten facts about the year 2023

1. Air temperatures in 2023 were the highest on record (since 1850/1880/1979, according to the particular data series). Recent warming is not symmetrical, but is mainly seen in the Northern Hemisphere (Figures 1 and 13).

Figure 1: 2023 surface air temperatures compared to the average for the previous 10 years. Green-yellow-red colours indicate areas with higher temperature than the average, while blue colours indicate lower than average temperatures. Data source: Remote Sensed Surface Temperature Anomaly, AIRS/Aqua L3 Monthly Standard Physical Retrieval 1-degree x 1-degree V006 (https://airs.jpl.nasa.gov/), obtained from the GISS data portal (https://data.giss.nasa.gov/gistemp/maps/).

 Figure 13: Zonal air temperatures. Global monthly average lower troposphere temperature since 1979 for the tropics and the northern and southern extratropics, according to University of Alabama at Huntsville, USA. Thin lines: monthly value; thick lines: 3-year running mean.

2. Arctic air temperatures have increased during the satellite era (since 1979), but Antarctic temperatures remain essentially stable (Figure 14).

Figure 14: Polar temperatures Global monthly average lower troposphere temperature since 1979 for the North and South Pole regions, according to University of Alabama at Huntsville (UAH), USA. Thick lines are the simple running 37-month average.

3. Since 2004, globally, the upper 1900m of the oceans has seen net warming of about 0.037°C. The greatest warming (of about 0.2°C) is in the uppermost 100m, and mainly in regions near the Equator, where the greatest amount of solar radiation is received (Figure 28).

Figure 28: Temperature changes 0–1900m Global ocean net temperature change since 2004 from surface to 1900m depth, using Argo-data. Source: Global Marine Argo Atlas.

4. Since 2004, the northern oceans (55–65°N) have, on average, experienced a marked cooling down to 1400m depth, and slight warming below that (Figure 29). Over the same period, the southern oceans (55–65°S) have, on average, seen some warming at most depths (above 1900m), but mainly near the surface.

Figure 29: Temperature changes 0–1900m Global ocean net temperature change since 2004 from surface to 1900m depth. Source: Global Marine Argo Atlas

5. Sea level globally is increasing at about 3.4 mm per year or more according to satellites, but only at 1-2 mm per year according to coastal tide gauges (Figures 39 and 41). Local and regional sea-level changes usually deviate significantly from such global averages.

Figure 39: Global sea level change since December 1992 The two lower panels show the annual sea level change, calculated for 1- and 10-year time windows, respectively. These values are plotted at the end of the interval considered. Source: Colorado Center for Astrodynamics Research at University of Colorado at Boulder. The blue dots are the individual observations (with calculated GIA e”ect removed), and the purple line represents the running 121-month (ca. 10-year) average.

Figure 41: Holgate-9 monthly tide gauge data from PSMSL Data Explorer The Holgate-9 are a series of tide gauges located in geologically stable sites. The two lower panels show the annual sea level change, calculated for 1- and 10-year time windows, respectively. These values are plotted at the end of the interval considered. Source: Colorado Center for Astrodynamics Research at University of Colorado at Boulder. The blue dots are the individual observations, and the purple line represents the running 121-month (ca. 10-year) average.

6. Global sea-ice extent remains well below the average for the satellite era (since 1979). Since 2018, however, it has remained quasistable, perhaps even exhibiting a small increase (Figure 43).

Figure 43: Global and hemispheric sea ice extent since 1979 12-month running means. The October 1979 value represents the monthly average of November 1978–October 1979, the November 1979 value represents the average of December 1978–November 1979, etc. The stippled lines represent a 61-month (ca. 5 years) average. The last month included in the 12-month calculations is shown to the right in the diagram. Data source: National Snow and Ice Data Center (NSIDC).

7. Global snow cover has remained essentially stable throughout the satellite era (Figure 47), although with important regional and seasonal variations.

Figure 47: Northern hemisphere weekly snow cover since 2000 (a) Since January 2000 and (b) Since 1972. Source: Rutgers University Global Snow Laboratory. The thin blue line is the weekly data, and the thick blue line is the running 53-week average (approximately 1 year). The horizontal red line is the 1972–2022 average.

8. Global precipitation varies from more than 3000mm per year in humid regions to almost nothing in deserts. Global average precipitation exhibits variations from one year to the next, and from decade to decade, but since 1901 there has been no clear overall trend (Figure 50).

Figure 50: Global precipitation anomalies. Variation of annual anomalies in relation to the global average precipitation from 1901 to 2021 based on rainfall and snowfall measurements from land-based weather stations worldwide. Data source: United States Environmental Protection Agency (EPA).

9. Storms and hurricanes display variable frequency over time, but without any clear global trend towards higher or lower values (Figure 51).

Figure 51: Annual global accumulated cyclone energy Source: Ryan Maue.

 

10. Observations confirm the continuing long-term variability of average meteorological and oceanographic conditions, but do not support the notion of an ongoing climate crisis.

Summing up

The global climate system is multifaceted, involving sun, planets, atmosphere, oceans, land, geological processes, biological life, and complex interactions between them. Many components and their mutual coupling are still not fully understood or perhaps not even recognised.

Believing that one minor constituent of the atmosphere (CO2) controls nearly all aspects of climate is naïve and entirely unrealistic.

The global climate has remained in a quasi-stable condition within certain limits for millions of years, although with important variations playing out over periods ranging from years to centuries or more, but the global climate has never been in a fully stable state without change.

Modern observations show that this behaviour continues today;
there is no evidence of a global climate crisis.

There is no charge for content on this site, nor for subscribers to receive email notifications of postings.

For Millions of Years Earth Temperatures Not Driven by CO2

Posted on by Ron Clutz

Figure 5 , W J Davis (2017)

The Relationship between Atmospheric Carbon Dioxide Concentration and Global Temperature for the Last 425 Million Years by W. Jackson Davis describes the evidence why earth temperatures are decoupled from CO2 throughout 425 Million years of history.  Excerpts in italics with my bolds.

Abstract:

Assessing human impacts on climate and biodiversity requires an understanding of the relationship between the concentration of carbon dioxide (CO2) in the Earth’s atmosphere and global temperature (T). Here I explore this relationship empirically using comprehensive, recently-compiled databases of stable-isotope proxies from the Phanerozoic Eon (~540 to 0 years before the present) and through complementary modeling using the atmospheric absorption/ transmittance code MODTRAN.

Atmospheric CO2 concentration is correlated weakly but negatively
with linearly-detrended T proxies over the last 425 million years.

Of 68 correlation coefficients (half non-parametric) between CO2 and T proxies encompassing all known major Phanerozoic climate transitions, 77.9% are non-discernible (p > 0.05) and 60.0% of discernible correlations are negative. Marginal radiative forcing (ΔRFCO2), the change in forcing at the top of the troposphere associated with a unit increase in atmospheric CO2 concentration, was computed using MODTRAN. The correlation between ΔRFCO2 and linearly-detrended T across the Phanerozoic Eon is positive and discernible, but only 2.6% of variance in T is attributable to variance in ΔRFCO2.

Spectral analysis, auto- and cross-correlation show that proxies for T, atmospheric CO2 concentration and ΔRFCO2 oscillate across the Phanerozoic, and cycles of CO2 and ΔRFCO2 are antiphasic. A prominent 15 million-year CO2 cycle coincides closely with identified mass extinctions of the past, suggesting a pressing need for research on the relationship between CO2, biodiversity extinction, and related carbon policies.

This study demonstrates that changes in atmospheric CO2 concentration did not cause temperature change in the ancient climate.

Introduction

The role of atmospheric CO2 in climate includes short- and long-term aspects. In the short term, atmospheric trace gases including CO2 are widely considered to affect weather by influencing surface sea temperature anomalies and sea-ice variation, which are key leading indicators of annual and decadal atmospheric circulation and consequent rainfall, drought, floods and other weather extremes [33–37]. Understanding the role of atmospheric CO2 in forcing global temperature, therefore has the potential to improve weather forecasting.

In the long term, the Intergovernmental Panel on Climate Change (IPCC) promulgates a significant role for CO2 in forcing global climate, estimating a “most likely” sensitivity of global temperature to a doubling of CO2 concentration as 2–4 °C [29–31]. Policies intended to adapt to the projected consequences of global warming and to mitigate the projected effects by reducing anthropogenic CO2 emissions are on the agenda of local, regional and national governments and international bodies.

The compilation in the last decade of comprehensive empirical databases containing proxies of Phanerozoic temperature and atmospheric CO2 concentration enables a fresh analytic approach to the CO2/T relationship. The temperature-proxy databases include thousands of measurements by hundreds of investigators for the time period from 522 to 0 Mybp [28,38,39], while proxies for atmospheric CO2 from the Phanerozoic Eon encompass 831 measurements reported independently by hundreds of investigators for the time period from 425 to 0 Mybp [40]. Such an unprecedented volume of data on the Phanerozoic climate enables the most accurate quantitative empirical evaluation to date of the relationship between atmospheric CO2 concentration and temperature in the ancient climate, which is the purpose of this study.

I report here that proxies for temperature and atmospheric CO2 concentration
are generally uncorrelated across the Phanerozoic climate,
showing that atmospheric CO2 did not drive the ancient climate.

The concentration of CO2 in the atmosphere is a less-direct measure of its effect on global temperature than marginal radiative forcing, however, which is nonetheless also generally uncorrelated with temperature across the Phanerozoic. The present findings from the Phanerozoic climate provide possible insights into the role of atmospheric CO2 in more recent glacial cycling and for contemporary climate science and carbon policies. Finally, I report that the concentration of atmospheric CO2 oscillated regularly during the Phanerozoic and peaks in CO2 concentration closely match the peaks of mass extinctions identified by previous investigators. This finding suggests an urgent need for research aimed at quantifying the relationship between atmospheric CO2  concentration and past mass extinctions. I conclude that that limiting anthropogenic emissions of CO2 may not be helpful in preventing harmful global warming, but may be essential to  conserving biodiversity.

Discussion of Temperature versus Atmospheric Carbon Dioxide

Temperature and atmospheric CO2 concentration proxies plotted in the same time series panel (Figure 5) show an apparent dissociation and even an antiphasic relationship. For example, a CO2 concentration peak near 415 My occurs near a temperature trough at 445 My. Similarly, CO2 concentration peaks around 285 Mybp coincide with a temperature trough at about 280 My and also  with the Permo-Carboniferous glacial period (labeled 2 in Figure 5). In more recent time periods, where data sampling resolution is greater, the same trend is visually evident. The atmospheric CO2  concentration peak near 200 My occurs during a cooling climate, as does another, smaller CO2 concentration peak at approximately 37 My. The shorter cooling periods of the Phanerozoic, labeled 1–10 in Figure 5, do not appear qualitatively, at least, to bear any definitive relationship with fluctuations in the atmospheric concentration of CO2.

[My Comment: Antiphasic in this context refers to times when temperatures are rising while CO2 is declining, and also periods when temperatures are falling while CO2 is going higher.  These negative correlations are to be expected if temperature is the leading variable and CO2 the dependent variable.]

Regression of linearly-detrended temperature proxies (Figure 3b, lower red curve) against atmospheric CO2 concentration proxy data reveals a weak but discernible negative correlation between CO2 concentration and T (Figure 6). Contrary to the conventional expectation, therefore, as the concentration of atmospheric CO2 increased during the Phanerozoic climate, T decreased. This finding is consistent with the apparent weak antiphasic relation between atmospheric CO2 concentration proxies and T suggested by visual examination of empirical data (Figure 5). The percent of variance in T that can be explained by variance in atmospheric CO2 concentration, or conversely, R2 × 100, is 3.6%. Therefore, more than 95% of the variance in T is explained by unidentified variables other than the atmospheric concentration of CO2.

Regression of non-detrended temperature against atmospheric CO2 concentration shows a weak but discernible positive correlation between CO2 concentration and T. This weak positive association may result from the general decline in temperature accompanied by a weak overall decline in CO2 concentration.

The correlation coefficients between the concentration of CO2 in the atmosphere and T were computed also across 15 shorter time segments of the Phanerozoic.

These time periods were selected to include or bracket the three major glacial periods of the Phanerozoic, ten global cooling events identified by stratigraphic indicators, and major transitions between warming and cooling of the Earth designated by the bar across the top of Figure 5. The analysis was done separately for the most recent time periods of the Phanerozoic, where the sampling resolution was highest (Table 1), and for the older time periods of the Phanerozoic, where the sampling resolution was lower (Table 2).

For the most highly-resolved Phanerozoic data (Table 1), 12/15 (80.0%) Pearson correlation coefficients computed between atmospheric CO2 concentration proxies and T proxies are non-discernible (p > 0.05). Of the three discernible correlation coefficients, all are negative, i.e., T and atmospheric CO2 concentration are inversely related across the corresponding time periods.

For the less highly-resolved older Phanerozoic data (Table 2), 14/20 (70.0%) Pearson correlation coefficients computed between atmospheric CO2 concentration and T are non-discernible. Of the six discernible correlation coefficients, two are negative. For the less-sampled older Phanerozoic (Table 2), 17/20 (85.0%) Spearman correlation coefficients are non-discernible. Of the three discernible Spearman correlation coefficients, one is negative.

Combining atmospheric CO2 concentration vs. T correlation coefficients
from both tables, 53/68 (77.9%) are non-discernible, and of
the 15 discernible correlation coefficients, nine (60.0%) are negative.

These data collectively support the conclusion that the atmospheric concentration of CO2 was largely decoupled from T over the majority of the Phanerozoic climate.

The finding that periodograms of atmospheric CO2 concentration proxies and T proxies exhibit different frequency profiles implies that atmospheric CO2 concentration and T oscillated at different frequencies during the Phanerozoic, consistent with disassociation between the respective cycles. This conclusion is corroborated by auto- and cross-correlation analysis.

If ΔRFCO2 is a more direct indicator of the impact of CO2 on temperature than atmospheric concentration as hypothesized, then the correlation between ΔRFCO2 and T over the Phanerozoic Eon might be expected to be positive and statistically discernible. This hypothesis is confirmed (Figure 9). This analysis entailed averaging atmospheric CO2 concentration in one-My bins over the recent Phanerozoic and either averaging or interpolating CO2 values over the older Phanerozoic (Methods). Owing to the relatively large sample size, the Pearson correlation coefficient is statistically discernible despite its small value (R = 0.16, n = 199), with the consequence that only a small fraction (2.56%) of the variance in T can be explained by variance in ΔRFCO2 (Figure 9). Even though the correlation coefficient between ΔRFCO2 and T is positive and discernible as hypothesized, therefore, the correlation coefficient can be considered negligible and the maximum effect of ΔRFCO2 on T is for practical purposes insignificant (<95%).

Conclusions

The principal findings of this study are that neither the atmospheric concentration
of CO2 nor ΔRFCO2 is correlated with T over most of the ancient (Phanerozoic) climate.

Over all major climate transitions of the Phanerozoic Eon, about three-quarters of 136 correlation coefficients computed here between T and atmospheric CO2 concentration, and between T and ΔRFCO2, are non-discernible, and about half of the discernible correlations are negative. Correlation does not imply causality, but the absence of correlation proves conclusively the absence of causality [63]. The finding that atmospheric CO2 concentration and ΔRFCO2 are generally uncorrelated with T, therefore, implies either that neither variable exerted significant causal influence on T during the Phanerozoic Eon or that the underlying proxy databases do not accurately reflect the variables evaluated.

The generally weak or absent correlations between the atmospheric concentration of CO2 and T,and between ΔRFCO2 and T, imply that other, unidentified variables caused most (>95%) of the variance in T across the Phanerozoic climate record. The dissimilar structures of periodograms for T and atmospheric CO2 concentration found here also imply that different but unidentified forces drove independent cyclic fluctuations in T and CO2. Since cycles in atmospheric CO2 concentrationoccur independently of temperature cycles, the respective rhythms must have a different etiology. It has been suggested that volcanic activity and seafloor spreading produce periodic CO2 emissions from the Earth’s mantle ([69] and references therein) which could in principle increase radiative forcing of temperature globally.

The present findings corroborate the earlier conclusion based on study of the Paleozoic climate that “global climate may be independent of variations in atmospheric carbon dioxide concentration.” [64] (p. 198). The present study shows further, however, that past atmospheric CO2 concentration oscillates on a cycle of 15–20 My and an amplitude of a few hundred to several hundreds of ppmv. A second longer cycle oscillates at 60–70 My. As discussed below, the peaks of the ~15 My cycles align closely with the times of identified mass extinctions during the Phanerozoic Eon, inviting further research on the relationship between atmospheric CO2 concentration and mass extinctions during the Phanerozoic.

My Added Comment

Some climatists will admit that CO2 changes did not cause ancient climate changes, but then assert that everything shifted when humans began burning hydrocarbons and releasing CO2.  Somehow natural processes ceased and now only warming can occur due to CO2 added by humans.  On the contrary, we can look more recently at the recovery from the LIA (Little Ice Age) to see the same antiphasic pattern described in the above paper.

Moberg is a highly respected recontruction of NH temperatures over the last 2000 years.  It shows peak warming after 1000, followed by a sharp cooling hitting bottom by 1600.  Kouwenberg is a CO2 time series based on plant stomata proxies.  For 250 years during the cooling, CO2 was rising, and then later CO2 was declining for 240 years while temperatures were rising.

As for the 20th century, consider the graph from climate4you (KNMI Climate Explorer)

Even with modern instrumental temperature records, correlation is inconsistent between temperature and CO2.  Much ado is made about the happenstance of positive linking between the 1990s to 2007, while ignoring the negative relation earlier, and a weak connection since.  The latter period is obviously driven by oceanic ENSO activity rather than CO2 radiation.

 

There is no charge for content on this site, nor for subscribers to receive email notifications of postings.

WMO Jumps the Warming Shark

Posted on by Ron Clutz

Considering the relentless fear mongering by the World Meteorological Organization (WMO), the acronym should  be pronounced “Whaammo.”  The latest is their hype about temperatures in 2023 as reported in the Daily Mail Climate change is ‘off the charts’:

Damning report reveals how records were smashed for greenhouse gas emissions, global temperatures and sea level rise in 2023 – and scientists warn ‘changes are speeding up’

Their killer graph is this one:

John Ray explains the exaggerations in comments at his blog In talics with my bolds and added images.

Here we go again. The temperature changes they are talking about are tiny and their link to human activities is just a wobbly theory. There is no proof that human activities had any impact at all.

All the warming since 1947 followed three strong El Nino events.

And note the chart. It is calibrated in TENTHS of one degree and has to go back to 1850 to show anything like a smooth rise. A more detailed chart would show long periods of stasis and falls, unlike CO emissions, which have been rising fairly steadily as industrial civilization has progressed. It is all just asssertion and even they admit that recent rises could be due to El Nino rather than CO2 emissions

And note that they show NO details of the CO2 changes which they allege to be at fault.

The sharp rise in ocean temps in 2023 has uncertain causes, but cannot be attributed to slow systemtic increases in CO2.

 

Humans Add Little to Rising CO2 March 2024

Posted on by Ron Clutz

 

Figure 16. Model reproduction of the monthly observations of evolution of δ13C at Barrow: (upper) without update of initial conditions and (lower) with update of initial conditions in each step by the δ13C observations.

While numerous studies support the title conclusion, the most recent and thorough analysis comes in the paper Net Isotopic Signature of Atmospheric CO2 Sources and Sinks: No Change since the Little Ice Age  by Demetris Koutsoyiannis.  Excerpts in italics with my bolds and added images. H/T notrickszone

Abstract

Recent studies have provided evidence, based on analyses of instrumental measurements of the last seven decades, for a unidirectional, potentially causal link between temperature as the cause and carbon dioxide concentration ([CO2]) as the effect. In the most recent study, this finding was supported by analysing the carbon cycle and showing that the natural [CO2] changes due to temperature rise are far larger (by a factor > 3) than human emissions, while the latter are no larger than 4% of the total. Here, we provide additional support for these findings by examining the signatures of the stable carbon isotopes, 12 and 13. Examining isotopic data in four important observation sites, we show that the standard metric δ13C is consistent with an input isotopic signature that is stable over the entire period of observations (>40 years), i.e., not affected by increases in human CO2 emissions. In addition, proxy data covering the period after 1500 AD also show stable behaviour.

These findings confirm the major role of the biosphere in the carbon cycle
and a non-discernible signature of humans.

Introduction
In recent years, a decrease in atmospheric δ13C has been observed, which is often termed the Suess Effect after Suess (1955) [11], who published the first observations on this phenomenon on trees, albeit using 14C data. He attributed the decrease to human activities, stating:
The decrease [in the specific 14C activity of wood at time of growth during the past 50 years] can be attributed to the introduction of a certain amount of C14-free CO2 into the atmosphere by artificial coal and oil combustion and to the rate of isotopic exchange between atmospheric CO2 and the bicarbonate dissolved in the oceans.
There is no question that δ13C has been decreasing and that human emissions have been increasing since the Industrial Revolution (Figure 2). Also, as seen in Figure 1, the combustion of fossil fuels can have an effect on reducing δ13C, as they are relatively depleted in 13C. This was the line of thought behind Suess [11] (even though the above quotation refers to 14C) and has become a common conviction thereafter. 

Figure 2. (left) Compiled data set of annual mean, global mean values for δ13C in atmospheric CO2, from Graven et al. [12], reconstructed after digitisation of Figure 3 of Graven et al. [8]; and (right) evolution of global human carbon emissions [13,14], after conversion from CO2 to C (dividing by 3.67).

For example, Andres et al. [15,16] stated:

The carbon isotopic (δ13C, PDB) signature of fossil fuel emissions has decreased during the last century, reflecting the changing mix of fossil fuels produced.

Also, in their recent review paper, Graven et al. [8] noted:

Since the Industrial Revolution, the carbon isotopic composition of atmospheric CO2 has undergone dramatic changes as a result of human activities and the response of the natural carbon cycle to them. The relative amount of atmospheric 14C and 13C in CO2 has decreased because of the addition of 14C- and 13C-depleted fossil carbon.

These generally accepted hypotheses, however, may reflect a dogmatic approach, or a postmodern ideological effect, i.e., to blame everything on human actions. Hence, the null hypothesis that all observed changes are (mostly) natural has not seriously been investigated. However, there are good reasons for this investigation. It is a fact that the biosphere has become more productive and expanded [5,17,18,19], resulting in natural amplification of the carbon cycle due to increased temperature. This fact may have been a primary factor for the decrease in the isotopic signature δ13C in atmospheric CO2. Note that the emissions of the biosphere are much larger than fossil fuel emissions (where the latter are only 4% of the total) [5] and, as seen in Figure 1, the biosphere’s isotopic signature δ13C is much lower than the atmospheric (see also Section 6).

Figure 1. Typical ranges of isotopic signatures δ13C for each of the pools interacting with atmospheric CO2, and related exchange processes.

In addition to the biosphere’s action, other natural factors also affect the input isotopic signature in the atmospheric CO2. These include volcano eruptions, among which, in the recent period, the Pinatubo eruption in 1991 is regarded as the most important, as well as the interannual variability related to El Niño—Southern Oscillation (ENSO) [8].

To investigate the null hypothesis and answer the two research questions posed above, we use modern instrumental and proxy data, as described in Section 2. We develop a theoretical framework in Section 3, which we apply to the data in a diagnostic mode in Section 4, and in a modelling mode in Section 5. The findings of these applications are further discussed in Section 6 and the conclusions are drawn in Section 7.

Discussion

With only two parameters, δ13CU and δ13CD, which represent the input isotopic signatures for the seasonal increasing and decreasing phases of [CO2], respectively, we are able to effectively model the isotopic signature δ13C of the atmosphere for the entire observation period. Of these parameters, δ13CD, reflecting the fractionation by photosynthesis, can be assumed as the same for the entire globe, while δ13CU varies, with smaller (more negative) values as we go north and higher (less negative) values as we go south. This spatial variation of δ13CU reflects the differences of the strength of seasonality in [CO2] and δ13C, which is at a maximum toward the North Pole and at a minimum at the South Pole.

The strong seasonality at high latitudes north is probably related to the processes in boreal vegetation, the dominance of snow and ice in winter, and the absence of photosynthesis during the 6-month night (note that Barrow, at a latitude of 71.3° N, is more north than the Artic Circle at 66.6° N). As we go south, some of these features cease to occur, and seasonality becomes less prominent, as photosynthesis occurs throughout the entire year, albeit with varying intensities. The minimal seasonality in the South Pole is probably related to the absence of vegetation due to the minimal appearance of land beyond a latitude of 43° S (with the exception of the frozen continent of Antarctica and a relatively small wedge of land in South America). All these suggest the dominance of terrestrial biosphere processes in driving [CO2] and δ13C.

Considering the fact that, as seen in Figure 2 (above), the human carbon emissions per year have doubled in the observed time period, if these were a key factor, this would somehow be reflected in a trend in the seasonality. Therefore, no sign is discerned that would necessitate an attribution to the influence of fossil fuel emissions. In contrast, continuity suggests that the key processes in CO2 emissions are related to biosphere processes such as respiration and photosynthesis.
.
Despite differences in seasonality, the over-annual input isotopic signature δ13CI remains almost the same globally, as seen in Table 4, which summarizes the results of all analyses, diagnostic and modelling, suggesting similar values, irrespective of the method used. This is not difficult to explain as, in the long run, CO2 is well mixed in the atmosphere; thus regional differences in seasonal δ13CI tend to disappear.

In both the diagnostic and the modelling phases of this paper, the inclusion of human emissions proved unnecessary. This may contrast with common opinion, which blames all changes on humans, but is absolutely reasonable, as humans are responsible for only 4% of carbon emissions. In addition, the vast majority of changes in the atmosphere since 1750 are due to natural processes, respiration and photosynthesis, as articulated in the recent study by Koutsoyiannis et al. [5] and schematically depicted in Figure 22, reproduced from that study.

Figure 22. Annual carbon balance in the Earth’s atmosphere, in Gt C/year, based on the IPCC estimates (Figure 5.12 of [30]). The balance of 5.1 Gt C/year is the annual accumulation of carbon (in the form of CO2) in the atmosphere (reproduced from [5].).

The following observations can be noted in Figure 22: (a) the terrestrial biosphere processes are much stronger than the maritime ones in terms of both production and absorption of CO2; (b) the CO2 emissions by even the ocean biosphere are much larger than human emissions; and (c) the modern (post 1750) CO2 additions to pre-industrial quantities (red bars in the right-hand part of the graph, corresponding to positive values) exceed the human emissions by a factor of ~4.5. These observations provide explanations for the findings of this study.
Furthermore, it is relevant to note the minor role of CO2 in the greenhouse effect. As shown in a recent study by Koutsoyiannis and Vournas, despite the increase in [CO2] by more than 30% in a century-long period, the strength of the greenhouse effect has not changed in a manner discernible in the radiation data. The greenhouse effect is dominated by the presence of water vapour in the atmosphere, rather than CO2. That study is Revisiting the greenhouse effect – a hydrological perspective in Hydrological Sciences Journal, 2023.
Conclusions
The results of the analyses in this paper provide negative answers to the research questions posed in the Introduction. Specifically:
♦  From modern instrumental carbon isotopic data of the last 40 years, no signs of human (fossil fuel) CO2 emissions can be discerned;
♦  Proxy data since the Little Ice Age suggest that the modern period of instrumental data does not differ, in terms of the net isotopic signature of atmospheric CO2 sources and sinks, from earlier centuries.
Combined with earlier studies, namely [2,3,4,5,31], these findings allow for the following line of thought to be formulated, which contrasts the dominant climate narrative, on the basis that different lines of thought are beneficial for the progress of science, even though they are not welcomed by those with political agendas promoting the narratives (whose representatives declare that they “own the science”, as can be seen in the motto in the beginning of the paper).
    1. In the 16th century, Earth entered a cool climatic period, known as the Little Ice Age, which ended at the beginning of the 19th century;
    2. Immediately after, a warming period began, which has lasted until now. The causes of the warming must be analogous to those that resulted in the Medieval Warm Period around 1000 AD, the Roman Climate Optimum around the first centuries BC and AD, the Minoan Climate Optimum at around 1500 BC, and other warming periods throughout the Holocene
    3.  As a result of the recent warming, and as explained in [5], the biosphere has expanded and become more productive, leading to increased CO2 concentration in the atmosphere and greening of the Earth [17,18,19,32];
    4. As a result of the increased CO2 concentration, the isotopic signature δ13C in the atmosphere has decreased;
    5. The greenhouse effect on the Earth remained stable in the last century, as it is dominated by the water vapour in the atmosphere [31];
    6. Human CO2 emissions have played a minor role in the recent climatic evolution, which is hardly discernible in observational data and unnecessary to invoke in modelling the observed behaviours, including the change in the isotopic signature δ13C in the atmosphere.
Overall, the findings in this paper confirm the major role of the biosphere
in the carbon cycle (and through this in climate)
and a non-discernible signature of humans.
One may associate the findings of the paper with several questions related to international policies:
♦  Do these results refute the hypothesis that CO2 emissions contribute to global warming through the greenhouse effect?
♦  Do these findings, by suggesting a minimal human impact on the isotopic composition of atmospheric carbon, contradict the need to reduce CO2 emissions?
♦  Are human carbon emissions independent from other forms of pollution, such as emissions of fine particles and nitrogen oxides, which can have harmful effects on human health and the environment?
These questions are not posed at all in the paper and certainly are not studied in it. Therefore, they cannot be answered on a scientific basis within the paper’s confined scope but require further research. The reader may feel free to study such questions and provide sensible replies. It is relevant to note that a reviewer implied these questions and suggested negative replies to each of them.

UAH February 2024: SH Saves Global Warming

Posted on by Ron Clutz

The post below updates the UAH record of air temperatures over land and ocean. Each month and year exposes again the growing disconnect between the real world and the Zero Carbon zealots.  It is as though the anti-hydrocarbon band wagon hopes to drown out the data contradicting their justification for the Great Energy Transition.  Yes, there has been warming from an El Nino buildup coincidental with North Atlantic warming, but no basis to blame it on CO2.  

As an overview consider how recent rapid cooling  completely overcame the warming from the last 3 El Ninos (1998, 2010 and 2016).  The UAH record shows that the effects of the last one were gone as of April 2021, again in November 2021, and in February and June 2022  At year end 2022 and continuing into 2023 global temp anomaly matched or went lower than average since 1995, an ENSO neutral year. (UAH baseline is now 1991-2020). Now we have an usual El Nino warming spike of uncertain cause, but unrelated to steadily rising CO2.

For reference I added an overlay of CO2 annual concentrations as measured at Mauna Loa.  While temperatures fluctuated up and down ending flat, CO2 went up steadily by ~60 ppm, a 15% increase.

Furthermore, going back to previous warmings prior to the satellite record shows that the entire rise of 0.8C since 1947 is due to oceanic, not human activity.

gmt-warming-events

The animation is an update of a previous analysis from Dr. Murry Salby.  These graphs use Hadcrut4 and include the 2016 El Nino warming event.  The exhibit shows since 1947 GMT warmed by 0.8 C, from 13.9 to 14.7, as estimated by Hadcrut4.  This resulted from three natural warming events involving ocean cycles. The most recent rise 2013-16 lifted temperatures by 0.2C.  Previously the 1997-98 El Nino produced a plateau increase of 0.4C.  Before that, a rise from 1977-81 added 0.2C to start the warming since 1947.

Importantly, the theory of human-caused global warming asserts that increasing CO2 in the atmosphere changes the baseline and causes systemic warming in our climate.  On the contrary, all of the warming since 1947 was episodic, coming from three brief events associated with oceanic cycles. And now in 2023 we are seeing an amazing episode with a temperature spike driven by ocean air warming in all regions, with some cooling the last two months. 

Update August 3, 2021

Chris Schoeneveld has produced a similar graph to the animation above, with a temperature series combining HadCRUT4 and UAH6. H/T WUWT

image-8

 

mc_wh_gas_web20210423124932

See Also Worst Threat: Greenhouse Gas or Quiet Sun?

February 2024 SH Warming Overcomes Cooling Elsewhere

banner-blog

With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea.  While you heard a lot about 2020-21 temperatures matching 2016 as the highest ever, that spin ignores how fast the cooling set in.  The UAH data analyzed below shows that warming from the last El Nino had fully dissipated with chilly temperatures in all regions. After a warming blip in 2022, land and ocean temps dropped again with 2023 starting below the mean since 1995.  Spring and Summer 2023 saw a series of warmings, continuing into October, but with cooling since. 

UAH has updated their tlt (temperatures in lower troposphere) dataset for February 2024. Posts on their reading of ocean air temps this month comes just after updated records from HadSST4.  I posted yesterday on SSTs using HadSST4 Ocean Cools as El Nino Recedes February 2024. This month also has a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years.

Sometimes air temps over land diverge from ocean air changes.  November 2023 was notable for a dichotomy between Ocean and Land air temperatures in UAH dataset. Remarkably a new high for Ocean air temps appeared with warming in all regions, while Land air temps dropped with cooling in all regions.  As a result the Global Ocean and Land anomaly result remained little changed. Last month in February 2024, both ocean and land air temps went higher driven by SH, while NH and the Tropics cooled slightly, resulting in Global anomaly matching October 2023 peak.

Note:  UAH has shifted their baseline from 1981-2010 to 1991-2020 beginning with January 2021.  In the charts below, the trends and fluctuations remain the same but the anomaly values changed with the baseline reference shift.

Presently sea surface temperatures (SST) are the best available indicator of heat content gained or lost from earth’s climate system.  Enthalpy is the thermodynamic term for total heat content in a system, and humidity differences in air parcels affect enthalpy.  Measuring water temperature directly avoids distorted impressions from air measurements.  In addition, ocean covers 71% of the planet surface and thus dominates surface temperature estimates.  Eventually we will likely have reliable means of recording water temperatures at depth.

Recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  Thus cooling oceans portend cooling land air temperatures to follow.  He also observed that changes in CO2 atmospheric concentrations lag behind SST by 11-12 months.  This latter point is addressed in a previous post Who to Blame for Rising CO2?

After a change in priorities, updates are now exclusive to HadSST4.  For comparison we can also look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for February.  The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above. Recently there was a change in UAH processing of satellite drift corrections, including dropping one platform which can no longer be corrected. The graphs below are taken from the revised and current dataset.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. There is the additional feature that ocean air temps avoid Urban Heat Islands (UHI).  The graph below shows monthly anomalies for ocean air temps since January 2015.

Note 2020 was warmed mainly by a spike in February in all regions, and secondarily by an October spike in NH alone. In 2021, SH and the Tropics both pulled the Global anomaly down to a new low in April. Then SH and Tropics upward spikes, along with NH warming brought Global temps to a peak in October.  That warmth was gone as November 2021 ocean temps plummeted everywhere. After an upward bump 01/2022 temps reversed and plunged downward in June.  After an upward spike in July, ocean air everywhere cooled in August and also in September.   

After sharp cooling everywhere in January 2023, all regions were into negative territory. Note the Tropics matched the lowest value, but since have spiked sharply upward +1.7C, with the largest increases in April to July, and continuing through adding to a new high January 2024. NH also spiked upward to a new high, while Global ocean rise was more modest due to slight SH cooling.  Now in February, NH and Tropics have cooled slightly, while greater warming in SH resulting in a small Global rise.

Land Air Temperatures Tracking in Seesaw Pattern

We sometimes overlook that in climate temperature records, while the oceans are measured directly with SSTs, land temps are measured only indirectly.  The land temperature records at surface stations sample air temps at 2 meters above ground.  UAH gives tlt anomalies for air over land separately from ocean air temps.  The graph updated for February is below.

Here we have fresh evidence of the greater volatility of the Land temperatures, along with extraordinary departures by SH land.  Land temps are dominated by NH with a 2021 spike in January,  then dropping before rising in the summer to peak in October 2021. As with the ocean air temps, all that was erased in November with a sharp cooling everywhere.  After a summer 2022 NH spike, land temps dropped everywhere, and in January, further cooling in SH and Tropics offset by an uptick in NH. 

Remarkably, in 2023, SH land air anomaly shot up 2.1C, from  -0.6C in January to +1.5 in September, then dropped sharply to 0.6 in January 2024, matching the SH peak in 2016. Now in February SH anomaly jumped up nearly 0.4C, pulling up the Global land anomaly despite continuing cooling elsewhere.

The Bigger Picture UAH Global Since 1980

The chart shows monthly Global anomalies starting 01/1980 to present.  The average monthly anomaly is -0.04, for this period of more than four decades.  The graph shows the 1998 El Nino after which the mean resumed, and again after the smaller 2010 event. The 2016 El Nino matched 1998 peak and in addition NH after effects lasted longer, followed by the NH warming 2019-20.   An upward bump in 2021 was reversed with temps having returned close to the mean as of 2/2022.  March and April brought warmer Global temps, later reversed

With the sharp drops in Nov., Dec. and January 2023 temps, there was no increase over 1980. Then in 2023 the buildup to the October/November peak exceeded the sharp April peak of the El Nino 1998 event. It also surpassed the February peak in 2016.  December and January were down slightly, and now February is matching the October peak. Where it goes from here, up or down further, remains to be seen, though there is evidence that El Nino is weakening.

The graph reminds of another chart showing the abrupt ejection of humid air from Hunga Tonga eruption.

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  Clearly NH and Global land temps have been dropping in a seesaw pattern, nearly 1C lower than the 2016 peak.  Since the ocean has 1000 times the heat capacity as the atmosphere, that cooling is a significant driving force.  TLT measures started the recent cooling later than SSTs from HadSST4, but are now showing the same pattern. Despite the three El Ninos, their warming has not persisted prior to 2023, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

Ocean Cools as El Nino Recedes February 2024

Posted on by Ron Clutz

The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • Major El Ninos have been the dominant climate feature in recent years.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source. Previously I used HadSST3 for these reports, but Hadley Centre has made HadSST4 the priority, and v.3 will no longer be updated.  HadSST4 is the same as v.3, except that the older data from ship water intake was re-estimated to be generally lower temperatures than shown in v.3.  The effect is that v.4 has lower average anomalies for the baseline period 1961-1990, thereby showing higher current anomalies than v.3. This analysis concerns more recent time periods and depends on very similar differentials as those from v.3 despite higher absolute anomaly values in v.4.  More on what distinguishes HadSST3 and 4 from other SST products at the end. The user guide for HadSST4 is here.

The Current Context

The chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through February 2024.  A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016.

Note that in 2015-2016 the Tropics and SH peaked in between two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period.  

Then in 2022, another strong NH summer spike peaked in August, but this time both the Tropic and SH were countervailing, resulting in only slight Global warming, later receding to the mean.   Oct./Nov. temps dropped  in NH and the Tropics took the Global anomaly below the average for this period. After an uptick in December, temps in January 2023 dropped everywhere, strongest in NH, with the Global anomaly further below the mean since 2015.

Then came El Nino as shown by the upward spike in the Tropics since January, the anomaly nearly tripling from 0.38C to 1.09C.  In September 2023, all regions rose, especially NH up from 0.70C to 1.41C, pulling up the global anomaly to a new high for this period. By December, NH cooled to 1.1C and the Global anomaly down to 0.94C from its peak of 1.10C, despite slight warming in SH and Tropics.

Then in January both Tropics and SH rose, resulting Global Anomaly going higher. Tropics anomaly reached a new peak of 1.29C. and all ocean regions were higher than 01/2016, the previous peak. Now in February all regions cooled bringing the Global anomaly back down 0.13C from its September peak.

Comment:

The climatists have seized on this unusual warming as proof their Zero Carbon agenda is needed, without addressing how impossible it would be for CO2 warming the air to raise ocean temperatures.  It is the ocean that warms the air, not the other way around.  Recently Steven Koonin had this to say about the phonomenon confirmed in the graph above:

El Nino is a phenomenon in the climate system that happens once every four or five years.  Heat builds up in the equatorial Pacific to the west of Indonesia and so on.  Then when enough of it builds up it surges across the Pacific and changes the currents and the winds.  As it surges toward South America it was discovered and named in the 19th century  It is well understood at this point that the phenomenon has nothing to do with CO2.

Now people talk about changes in that phenomena as a result of CO2 but it’s there in the climate system already and when it happens it influences weather all over the world.   We feel it when it gets rainier in Southern California for example.  So for the last 3 years we have been in the opposite of an El Nino, a La Nina, part of the reason people think the West Coast has been in drought.

It has now shifted in the last months to an El Nino condition that warms the globe and is thought to contribute to this Spike we have seen. But there are other contributions as well.  One of the most surprising ones is that back in January of 2022 an enormous underwater volcano went off in Tonga and it put up a lot of water vapor into the upper atmosphere. It increased the upper atmosphere of water vapor by about 10 percent, and that’s a warming effect, and it may be that is contributing to why the spike is so high.

A longer view of SSTs

To enlarge image, open in new tabl.

The graph above is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations.  Previous posts focused on the rise and fall of the last El Nino starting in 2015.  This post adds a longer view, encompassing the significant 1998 El Nino and since.  The color schemes are retained for Global, Tropics, NH and SH anomalies.  Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July. 1995 is a reasonable (ENSO neutral) starting point prior to the first El Nino. 

The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99. There were strong cool periods before and after the 1998 El Nino event. Then SSTs in all regions returned to the mean in 2001-2. 

SSTS fluctuate around the mean until 2007, when another, smaller ENSO event occurs. There is cooling 2007-8,  a lower peak warming in 2009-10, following by cooling in 2011-12.  Again SSTs are average 2013-14.

Now a different pattern appears.  The Tropics cooled sharply to Jan 11, then rise steadily for 4 years to Jan 15, at which point the most recent major El Nino takes off.  But this time in contrast to ’97-’99, the Northern Hemisphere produces peaks every summer pulling up the Global average.  In fact, these NH peaks appear every July starting in 2003, growing stronger to produce 3 massive highs in 2014, 15 and 16.  NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peaks came in 2019 and 2020, only this time the Tropics and SH were offsetting rather adding to the warming. (Note: these are high anomalies on top of the highest absolute temps in the NH.)  Since 2014 SH has played a moderating role, offsetting the NH warming pulses. After September 2020 temps dropped off down until February 2021.  In 2021-22 there were again summer NH spikes, but in 2022 moderated first by cooling Tropics and SH SSTs, then in October to January 2023 by deeper cooling in NH and Tropics.  

Now in 2023 the Tropics flipped from below to well above average, while NH has produced a summer peak extending into September higher than any previous year.  Despite El Nino driving the Tropics January anomaly higher than 1998 and 2016 peaks, last month cooling in all regions, especially the Tropics suggests that the peak may have been reached.

What to make of all this? The patterns suggest that in addition to El Ninos in the Pacific driving the Tropic SSTs, something else is going on in the NH.  The obvious culprit is the North Atlantic, since I have seen this sort of pulsing before.  After reading some papers by David Dilley, I confirmed his observation of Atlantic pulses into the Arctic every 8 to 10 years.

Contemporary AMO Observations

Through January 2023 I depended on the Kaplan AMO Index (not smoothed, not detrended) for N. Atlantic observations. But it is no longer being updated, and NOAA says they don’t know its future.  So I find that ERSSTv5 AMO dataset has data through October.  It differs from Kaplan, which reported average absolute temps measured in N. Atlantic.  “ERSST5 AMO  follows Trenberth and Shea (2006) proposal to use the NA region EQ-60°N, 0°-80°W and subtract the global rise of SST 60°S-60°N to obtain a measure of the internal variability, arguing that the effect of external forcing on the North Atlantic should be similar to the effect on the other oceans.”  So the values represent sst anomaly differences between the N. Atlantic and the Global ocean.

The chart above confirms what Kaplan also showed.  As August is the hottest month for the N. Atlantic, its varibility, high and low, drives the annual results for this basin.  Note also the peaks in 2010, lows after 2014, and a rise in 2021. Now in 2023 the peak was holding at 1.4C before decling.  An annual chart below is informative:

Note the difference between blue/green years, beige/brown, and purple/red years.  2010, 2021, 2022 all peaked strongly in August or September.  1998 and 2007 were mildly warm.  2016 and 2018 were matching or cooler than the global average.  2023 started out slightly warm, then rose steadily to an  extraordinary peak in July.  August to October were only slightly lower, but by December cooled by ~0.4C. January 2024 was unchanged from the previous month, but February anomaly rose 0.1C

The pattern suggests the ocean may be demonstrating a stairstep pattern like that we have also seen in HadCRUT4. 

The purple line is the average anomaly 1980-1996 inclusive, value 0.18.  The orange line the average 1980-202306, value 0.38, also for the period 1997-2012. The red line is 2013-202306, value 0.64. As noted above, these rising stages are driven by the combined warming in the Tropics and NH, including both Pacific and Atlantic basins.

See Also:

2024 El Nino Collapsing

Curiosity:  Solar Coincidence?

The news about our current solar cycle 25 is that the solar activity is hitting peak numbers now and higher  than expected 1-2 years in the future.  As livescience put it:  Solar maximum could hit us harder and sooner than we thought. How dangerous will the sun’s chaotic peak be?  Some charts from spaceweatherlive look familar to these sea surface temperature charts.

Summary

The oceans are driving the warming this century.  SSTs took a step up with the 1998 El Nino and have stayed there with help from the North Atlantic, and more recently the Pacific northern “Blob.”  The ocean surfaces are releasing a lot of energy, warming the air, but eventually will have a cooling effect.  The decline after 1937 was rapid by comparison, so one wonders: How long can the oceans keep this up? And is the sun adding forcing to this process?

Space weather impacts the ionosphere in this animation. Credits: NASA/GSFC/CIL/Krystofer Kim

Footnote: Why Rely on HadSST4

HadSST is distinguished from other SST products because HadCRU (Hadley Climatic Research Unit) does not engage in SST interpolation, i.e. infilling estimated anomalies into grid cells lacking sufficient sampling in a given month. From reading the documentation and from queries to Met Office, this is their procedure.

HadSST4 imports data from gridcells containing ocean, excluding land cells. From past records, they have calculated daily and monthly average readings for each grid cell for the period 1961 to 1990. Those temperatures form the baseline from which anomalies are calculated.

In a given month, each gridcell with sufficient sampling is averaged for the month and then the baseline value for that cell and that month is subtracted, resulting in the monthly anomaly for that cell. All cells with monthly anomalies are averaged to produce global, hemispheric and tropical anomalies for the month, based on the cells in those locations. For example, Tropics averages include ocean grid cells lying between latitudes 20N and 20S.

Gridcells lacking sufficient sampling that month are left out of the averaging, and the uncertainty from such missing data is estimated. IMO that is more reasonable than inventing data to infill. And it seems that the Global Drifter Array displayed in the top image is providing more uniform coverage of the oceans than in the past.

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

 

 

2024 El Nino Collapsing

Posted on by Ron Clutz

Meteorologist Cliff Mass explains at his blog El Nino’s Collapse Has Begun.  Excerpts in italics with my bolds, added images and ending comment.

The entire character of this winter has been characterized by a strong El Nino.

El Nino impacts have included low snowpack over Washington State, huge snowpack and heavy precipitation over California, and warm temperatures over the Upper Plains states.

However, El Nino’s days are numbered and
its decline is proceeding rapidly right now.

First, consider the critical measure of El Nino: the sea surface temperatures in the central tropical Pacific (see graph above showing the Nino 3.4 area). The warmth of this El Nino peaked in late November (about 2.1°C above normal) and is now declining fairly rapidly (currently at roughly 1.3°C above normal).

But the cooling is really more dramatic than that:
a LOT of cooling has been happening beneath the surface!

To demonstrate this, take a look at subsurface temperatures (the difference from normal) for the lowest 300 m under the surface for a vertical cross-section across the Pacific (below).

On 8 January, there was a substantial warm layer extending about 100 m beneath the surface.

But look at the same cross-section on 27 February.

Wow–what a difference! The warm water has dramatically cooled, with only a thin veneer of warmth evident for much of the Pacific. Rapidly cooling has occurred beneath the surface and this cool water is about to spread to the surface.

If you really want to appreciate the profound cooling take a look at the amount of heat in the upper ocean for the western tropical Pacific (below, the difference from normal is being shown).

A very, very dramatic change has occurred. The heat content of the upper ocean peaked in late November and then plummeted. Declined so much that the water below the surface is now COOLER than normal.

El Nino fans will be further dismayed to learn that models are going for a continuous decline….so much so that they predict a La Nina next year!

My Comment: Why this shift from El Nino to La Nina matters

Global temperatures typically increase during an El Niño episode, and fall during La Niña.  El Niño means warmer water spreads further, and stays closer to the surface. This releases more heat into the atmosphere, creating wetter and warmer air.

Air temperatures typically peak a few months after El Niño hits maximum strength, as heat escapes from the sea surface to the atmosphere.

In 2021, the UN’s climate scientists, the IPCC, said the ENSO events which have occurred since 1950 are stronger than those observed between 1850 and 1950.  But it also said that tree rings and other historical evidence show there have been variations in the frequency and strength of these episodes since the 1400s.

The IPCC concluded there is no clear evidence that Climate Change™ has affected these events.

CO2 Coalition Does Climate Reveal in Wyoming

Posted on by Ron Clutz

The CO2 Coalition article is CO2 Coalition Takes the Science to Wyoming.  Excerpts in italics with my bolds and added images.

Wyoming has vast resources of coal, oil and natural gas. With 40% of the nation’s coal resources, the state has been the United States’ top producer since 1986, primarily from the Powder River Basin located in the northeastern part of the state. It is also a national leader in the production of oil and natural gas, ranking in the top 10 in production of both products. 

Yet, even though the Wyoming economy is heavily dependent on the mining and extraction of fossil fuels, its governor, Mark Gordon, has adopted a strong “decarbonization” policy. The science tells us that this is not a winning strategy for the people of Wyoming. 

The CO2 Coalition believes that public policy on such matters should be driven by scientific review and analysis, not political agendas. To provide such an analysis, we have produced this report, Wyoming and Climate Change: CO2 Should Be Celebrated, Not Captured. 

We also sent a team of climate experts from the CO2 Coalition,  including Dr. William Happer, Dr. Byron Soepyan and Gregory Wrightstone to Wyoming to provide the facts concerning the huge benefits of carbon dioxide. This team presented the science at a hearing of the Wyoming Senate Agriculture Committee (pictured above.)

The team also presented accurate science regarding Wyoming’s climate to students at Gillette College, Laramie County Community College, and at the University of Wyoming.

Temperature Data Shows Good News for Wyoming

Data for Wyoming contradict the 4th National Climate Assessment (NCA4) assertion that “the frequency and intensity of extreme high temperature events are virtually certain to increase.”

Our data analysis shows that high daily temperatures peaked during the Dust Bowl years of the 1930s and have been in a 90-year decline. This is confirmed by reviewing the percentage of days that were reported to be hotter than 100°F (37.8°C) by Wyoming temperature stations. There is no discernible increase, and the largest numbers occurred in the first half of the 20th century when CO2 levels were 70% of recent measurements.

There has been, however, a beneficial increase in the minimum nighttime temperatures, which has led to a lengthening of the Wyoming growing season. Since the late 1800s, these nighttime temperatures have increased about 2°F (1.1°C).

The slight increase of about 1.2°F (0.7°C) in the average temperature
in the last 120 years is being driven by reductions in extreme cold
rather than increases in extreme heat.

Full Report:  Wyoming and Climate Change

Conclusion From Full Report 

The recent proposal by Wyoming Governor Mark Gordon to use “carbon capture” to achieve what he terms “negative net zero” (Gordon 2021) is based on a flawed theory that increasing CO2 in the atmosphere is leading to harmful effects on Wyoming’s environment and its people. Within this report, we have documented that modest warming and increasing carbon dioxide are clearly beneficial for the Cowboy State’s ecosystems and citizenry.

The data tell us the following:

• Current levels of carbon dioxide are at near historically low concentrations.
• Adjustments to historic temperature records have artificially amplified modern warming.
• Wyoming temperatures have increased a modest 1.2°F (0.7°C) since 1895.
• Heat waves peaked in the 1930s and have been in slight decline since that period.
• Nightime low temperatures have increased, lengthening growing seasons.
• Precipitation data, while varying greatly from year-to-year, show no increasing or decreasing trend.
• Droughts are not increasing in Wyoming.
• Severe weather and natural disasters are declining.
• Agricultural production, globally and in Wyoming, is thriving due to modest warming and more CO2.
• Vegetation in Wyoming and around the world is increasing.
• Greenhouse-induced warming that would be averted (< 0.003°F) by eliminating Wyoming’s CO2 emissions would be too small to measure and achieved, if at all, at enormous cost.
• Models used to project future temperatures significantly overpredict the amount of warming in coming decades.

CO2 Should Be Celebrated, Not Captured

Top Climate Model Improved to Show ENSO Skill

Posted on by Ron Clutz

Previous posts (linked at end) discuss how the climate model from RAS (Russian Academy of Science) has evolved through several versions. The interest arose because of its greater ability to replicate the past temperature history. The model is part of the CMIP program which is now going the next step to CMIP7, and is one of the first to test with a new climate simulation. Improvements to the latest model, INMCM60, show an enhanced ability to replicate ENSO oscillations in the Pacific ocean, which have significant climate impacts world wide.

This news comes by way of a new paper published in the Russian Journal of Numerical Analysis and Mathematical Modelling February 2024.  The title is ENSO phase locking, asymmetry and predictability in the INMCM Earth system model,  Seleznev et al. (2024) Excerpts in italics with my bolds and images from the article.

Abstract:

Advanced numerical climate models are known to exhibit biases in simulating some features of El Niño–Southern Oscillation (ENSO) which is a key mode of inter-annual climate variability. In this study we analyze how two fundamental features of observed ENSO – asymmetry between hot and cold states and phase-locking to the annual cycle – are reflected in two different versions of the INMCM Earth system model (state-of-the-art Earth system model participating in the Coupled Model Intercomparison Project).

We identify the above ENSO features using the conventional empirical orthogonal functions (EOF) analysis which is applied to both observed and simulated upper ocean heat content (OHC) data in the tropical Pacific. We obtain that the observed tropical Pacific OHC variability is described well by two leading EOF-modes which roughly reflect the fundamental recharge-discharge mechanism of ENSO. These modes exhibit strong seasonal cycles associated with ENSO phase locking while the revealed nonlinear dependencies between amplitudes of these cycles reflect ENSO asymmetry.

We also assess and compare predictability of observed and simulated ENSO based on linear inverse modeling. We find that the improved INMCM6 model has significant benefits in simulating described features of observed ENSO as compared with the previous INMCM5 model. The improvements of the INMCM6 model providing such benefits arediscussed. We argue that proper cloud parametrization scheme is crucial for accurate simulation of ENSO dynamics with numerical climate models

Introduction

El Niño–Southern Oscillation (ENSO) is the most prominent mode of inter-annual climate variability which originates in the tropical Pacific, but has a global impact [41]. Accurately simulating ENSO is still a challenging task for global climate modelers [3,5,15,25]. In the comprehensive study [35] large-ensemble climate model simulations provided by the Coupled Model Intercomparison Project phases 5 (CMIP5)and 6 (CMIP6) were analyzed. It was found that the CMIP6 models significantly outperform those fromCMIP5 for 8 out of 24 ENSO-relevant metrics, especially regarding the simulation of ENSO spatial patterns, diversity and teleconnections. Nevertheless, some important aspects of the observed ENSO are still not satisfactorily simulated by the most of state-of-the-art models [7,38,49]. In this study we are aimed at examination of how two such aspects – ENSO asymmetry and ENSO phase-locking to the annual cycle –are reflected in the INMCM Earth system model [44, 45].

The asymmetry between hot (El Nino) and cold (La Nina) states is a fundamental feature in the observed ENSO occurrences [39]. El Niño events are often stronger than La Niña events, while the latter ones tend to be more persistent [10]. Such an asymmetry is generally attributed to nonlinear feedbacks between sea surface temperatures (SSTs), thermocline and winds in the tropical Pacific [2,19,28]. The alternative conceptions highlight the role of tropical instability waves [1] and fast atmospheric processes associated with irregular zonal wind anomalies [24]. ENSO phase-locking is identified as the tendency of ENSO-events to peak in boreal winter.

Several studies [11,17,34] argue that the phase-locking is associated with seasonal changes in thermocline depth, ocean upwelling velocity, and cloud feedback processes. These processes collectively contribute to the coupling strength modulation between ocean and atmosphere, which, in the context of conceptual ENSO models [4,18], provides seasonal modulation of stability (in the sense of decay rate) of the “ENSO oscillator”. Another theory [20,42] supposes the phase-locking results from nonlinear interactions between the seasonal forcing and the inherent ENSO cycle. Both the asymmetry and phaselocking effects are typically captured by low-dimensional data-driven ENSO models [14, 21, 26, 29, 37].

In this work we identify the ENSO features discussed above via the analysis of upper ocean heat content (OHC) variability in the the tropical Pacific. The recent study [37] analyzed high-resolution reanalysis dataset of the tropical Pacific (10N – 10S, 120E – 80W) OHC anomalies in the 0–300 m depth layer using the standard empirical orthogonal function (EOF) decomposition [16]. It was found that observed OHC variability is effectively captured by two leading EOFs, which roughly describe the fundamental recharge-discharge mechanism of ENSO [18]. The time series of the corresponding principal components (PCs) demonstrate strong seasonal cycles, reflecting ENSO phase-locking, while the revealed inter-annual nonlinear dependencies between these cycles can be associated with ENSO asymmetry [37].

Here we apply similar analysis to the OHC data simulated by two different versions of INMCM Earth system model. The first is the INMCM5 model [45] from CMIP6, and the second is the perspective INMCM6 [44] model with improved parameterization of clouds, large-scale condensation and aerosols. Along with the traditional EOF decomposition we invoke the linear inverse modeling to assess and compare predictability of ENSO from observed and simulated data.

The paper is organized as follows. Sect. 2 describes the datasets we analyze: OHC reanalysis dataset and OHC data obtained from the ensemble simulations of global climate with two versions of INMCM model. Data preparation, including separation of the forced and internal variability, is also discussed. The ensemble EOF analysis is represented, which is used for identifying the meaningful processes contributing to observed and simulated ENSO dynamics. Sect. 3 presents the results we obtain in analyzing both observed and simulated OHC data. In Sect. 4 we summarize and discuss the obtained results, particularly regarding the significant benefits of new version of INMCM model (INMCM6) in simulating key features of observed ENSO.

Fig. 1: Two leading EOFs of the observed tropical Pacific upper ocean heat content (OHC) variability

Fig. 2: Two leading EOFs of the INMCM5 ensemble of tropical Pacific upper ocean heat content simulations

Fig. 3: The same as in Fig. 2 but for INMCM6 model simulations

The corresponding spatial patterns in Fig. 1 have clear interpretation. The first contributes to the central and eastern tropical Pacific, where most significant variations of sea surface temperature (SST) during El Niño/La Nina events occur [9]. The second predominates mainly in the western tropical Pacific and can be associated with the OHC accumulation and discharge before and during the El Niño events [48].

What we can see from Fig. 2 is that the two leading EOFs of OHC variability simulated by the INMCM5 model do not correspond to the observed ones. The corresponding time series and spatial patterns exhibit smaller-scale features, as compared to those we obtain from the reanalysys data, indicating their noisier spatio-temporal nature.

The two leading EOFs of the improved INMCM6 model (Fig. 3), by contrast, capture well both the spatial and temporal features of observed EOFs. In the next section we focus on furtheranalysis of these EOFs assuming that they contain the most meaningful information about ENSO dynamics.

Discussion

In this study we have analyzed how two different versions of the INMCM model [44,45] (state-of-the-art Earth system model participating in the Coupled Model Intercomparison Project, CMIP) simulate some features of El Niño–Southern Oscillation (ENSO) which is a key mode of the global climate. We identified the ENSO features via the EOF analysis applied to both observed and simulated upper ocean heat content(OHC) variability in the the tropical Pacific. It was found that the observed tropical Pacific OHC variability is captured well by two leading modes (EOFs) which reflect the fundamental recharge-discharge mechanism of ENSO involving a recharge and discharge of OHC along the equator caused by a disequilibrium between zonal winds and zonal mean thermocline depth. These modes are phase-shifted and exhibit the strong seasonal cycles associated with ENSO phase locking. The inter-annual dependencies between amplitudes of the revealed ESNO seasonal cycles are strongly nonlinear which reflects the asymmetry between hot (ElNino) and cold (La Nina) states of observed ENSO. We found that the INMCM5 model (the previous version of the INMCM model from CMIP6) poorly reproduces the leading modes of observed ENSO and reflect neither the observed ENSO phase locking nor asymmetry. At the same time, the perspective INMCM6 model demonstrates significant improvement in simulating these key features of observed ENSO. The analysis of ENSO predictability based on linear inverse modeling indicates that the improved INMCM6 model reflects well the ENSO spring predictability barrier and therefore could potentially have an advantage in long range weather prediction as compared with the INMCM5.

Such benefits of the new version of the INMCM model (INMCM6) in simulating observed ENSO dynamics can be provided by using more relevant parametrization of sub-grid scale processes. Particularly, the difference in the amplitude of OHC anomaly associated with ENSO between INMCM5 and INMCM6 shown in Fig.2-3 can be explained mainly by the difference in cloud parameterization in these models. In short, in INMCM5 El-Nino event leads to increase of middle and low clouds over central and eastern Pacific that leads to cooling because of decrease in surface incoming shortwave radiation.

While decrease in low clouds and increase in high clouds in INMCM6 over El-Nino region during positive phase of ENSO lead to further upper ocean warming [43]. This is consistent with the recent study [36] which argued that erroneous cloud feedback arising from a dominant contribution of low-level clouds may lead to heat flux feedback bias in the tropical Pacific, which play a key role in ENSO dynamics. Fast decrease in OHC in central Pacific after El-Nino maximum in INMCM6 can probably occur because of too shallow mixed layer in equatorial Pacific in the model, that leads to fast surface cooling after renewal of upwelling and further increase of tradewinds. Summarizing the above we can conclude that proper cloud parameterization scheme is crucial for accurate simulation of observed ENSO with numerical climate models.

Background on INMCM6

New 2023 INMCM RAS Climate Model First Results

The INMCM60 model, like the previous INMCM48 [1], consists of three major components: atmospheric dynamics, aerosol evolution, and ocean dynamics. The atmospheric component incorporates a land model including surface, vegetation, and soil. The oceanic component also encompasses a sea-ice evolution model. Both versions in the atmosphere have a spatial 2° × 1° longitude-by-latitude resolution and 21 vertical levels up to 10 hPa. In the ocean, the resolution is 1° × 0.5° and 40 levels.

The following changes have been introduced into the model compared to INMCM48.

Parameterization of clouds and large-scale condensation is identical to that described in [4], except that tuning parameters of this parameterization differ from any of the versions outlined in [3], being, however, closest to version 4. The main difference from it is that the cloud water flux rating boundary-layer clouds is estimated not only for reasons of boundary-layer turbulence development, but also from the condition of moist instability, which, under deep convection, results in fewer clouds in the boundary layer and more in the upper troposphere. The equilibrium sensitivity of such a version to a doubling of atmospheric СО2 is about 3.3 K.

The aerosol scheme has also been updated by including a change in the calculation of natural emissions of sulfate aerosol [5] and wet scavenging, as well as the influence of aerosol concentration on the cloud droplet radius, i.e., the first indirect effect [6]. Numerical values of the constants, however, were taken to be a little different from those used in [5]. Additionally, the improved scheme of snow evolution taking into account refreezing and the calculation of the snow albedo [7] were introduced to the model. The calculation of universal functions in the atmospheric boundary layer in stable stratification has also been changed: in the latest model version, such functions assume turbulence at even large gradient Richardson numbers [8].

 

El Nino Keeps Ocean Warm January 2024

Posted on by Ron Clutz

The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • Major El Ninos have been the dominant climate feature in recent years.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source. Previously I used HadSST3 for these reports, but Hadley Centre has made HadSST4 the priority, and v.3 will no longer be updated.  HadSST4 is the same as v.3, except that the older data from ship water intake was re-estimated to be generally lower temperatures than shown in v.3.  The effect is that v.4 has lower average anomalies for the baseline period 1961-1990, thereby showing higher current anomalies than v.3. This analysis concerns more recent time periods and depends on very similar differentials as those from v.3 despite higher absolute anomaly values in v.4.  More on what distinguishes HadSST3 and 4 from other SST products at the end. The user guide for HadSST4 is here.

The Current Context

The chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through December 2023.  A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016.

Note that in 2015-2016 the Tropics and SH peaked in between two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period.  

Then in 2022, another strong NH summer spike peaked in August, but this time both the Tropic and SH were countervailing, resulting in only slight Global warming, later receding to the mean.   Oct./Nov. temps dropped  in NH and the Tropics took the Global anomaly below the average for this period. After an uptick in December, temps in January 2023 dropped everywhere, strongest in NH, with the Global anomaly further below the mean since 2015.

Then came El Nino as shown by the upward spike in the Tropics since January, the anomaly nearly tripling from 0.38C to 1.09C.  In September 2023, all regions rose, especially NH up from 0.70C to 1.41C, pulling up the global anomaly to a new high for this period. But then in October anomalies in all regions started dropping down bringing down the Global anomaly.  By December, NH cooled to 1.1C and the Global anomaly down to 0.94C from its peak of 1.10C, despite slight warming in SH and Tropics.

Now in January both Tropics and SH rose, resulting Global Anomaly going higher. Tropics anomaly reached a new peak of 1.29C. Note that all ocean regions are now higher than 01/2016, the previous peak.

Comment:

The climatists have seized on this unusual warming as proof of their Zero Carbon agenda, without addressing how impossible it would be for CO2 warming the air to raise ocean temperatures.  It is the ocean that warms the air, not the other way around.  Recently Steven Koonin had this to say about the phonomenon confirmed in the graph above:

El Nino is a phenomenon in the climate system that happens once every four or five years.  Heat builds up in the equatorial Pacific to the west of Indonesia and so on.  Then when enough of it builds up it surges across the Pacific and changes the currents and the winds.  As it surges toward South America it was discovered and named in the 19th century  It is well understood at this point that the phenomenon has nothing to do with CO2.

Now people talk about changes in that phenomena as a result of CO2 but it’s there in the climate system already and when it happens it influences weather all over the world.   We feel it when it gets rainier in Southern California for example.  So for the last 3 years we have been in the opposite of an El Nino, a La Nina, part of the reason people think the West Coast has been in drought.

It has now shifted in the last months to an El Nino condition that warms the globe and is thought to contribute to this Spike we have seen. But there are other contributions as well.  One of the most surprising ones is that back in January of 2022 an enormous underwater volcano went off in Tonga and it put up a lot of water vapor into the upper atmosphere. It increased the upper atmosphere of water vapor by about 10 percent, and that’s a warming effect, and it may be that is contributing to why the spike is so high.

A longer view of SSTs

To enlarge, open image in new tab.

The graph above is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations.  Previous posts focused on the rise and fall of the last El Nino starting in 2015.  This post adds a longer view, encompassing the significant 1998 El Nino and since.  The color schemes are retained for Global, Tropics, NH and SH anomalies.  Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July. 1995 is a reasonable (ENSO neutral) starting point prior to the first El Nino. 

The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99. There were strong cool periods before and after the 1998 El Nino event. Then SSTs in all regions returned to the mean in 2001-2. 

SSTS fluctuate around the mean until 2007, when another, smaller ENSO event occurs. There is cooling 2007-8,  a lower peak warming in 2009-10, following by cooling in 2011-12.  Again SSTs are average 2013-14.

Now a different pattern appears.  The Tropics cooled sharply to Jan 11, then rise steadily for 4 years to Jan 15, at which point the most recent major El Nino takes off.  But this time in contrast to ’97-’99, the Northern Hemisphere produces peaks every summer pulling up the Global average.  In fact, these NH peaks appear every July starting in 2003, growing stronger to produce 3 massive highs in 2014, 15 and 16.  NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peaks came in 2019 and 2020, only this time the Tropics and SH were offsetting rather adding to the warming. (Note: these are high anomalies on top of the highest absolute temps in the NH.)  Since 2014 SH has played a moderating role, offsetting the NH warming pulses. After September 2020 temps dropped off down until February 2021.  In 2021-22 there were again summer NH spikes, but in 2022 moderated first by cooling Tropics and SH SSTs, then in October to January 2023 by deeper cooling in NH and Tropics.  

Now in 2023 the Tropics flipped from below to well above average, while NH has produced a summer peak extending into September higher than any previous year. In fact, October and now November are showing that this number is likely the crest, despite El Nino driving the Tropics anomaly close to 1998 and 2015 peaks.

What to make of all this? The patterns suggest that in addition to El Ninos in the Pacific driving the Tropic SSTs, something else is going on in the NH.  The obvious culprit is the North Atlantic, since I have seen this sort of pulsing before.  After reading some papers by David Dilley, I confirmed his observation of Atlantic pulses into the Arctic every 8 to 10 years.

Contemporary AMO Observations

Through January 2023 I depended on the Kaplan AMO Index (not smoothed, not detrended) for N. Atlantic observations. But it is no longer being updated, and NOAA says they don’t know its future.  So I find that ERSSTv5 AMO dataset has data through October.  It differs from Kaplan, which reported average absolute temps measured in N. Atlantic.  “ERSST5 AMO  follows Trenberth and Shea (2006) proposal to use the NA region EQ-60°N, 0°-80°W and subtract the global rise of SST 60°S-60°N to obtain a measure of the internal variability, arguing that the effect of external forcing on the North Atlantic should be similar to the effect on the other oceans.”  So the values represent sst anomaly differences between the N. Atlantic and the Global ocean.

The chart above confirms what Kaplan also showed.  As August is the hottest month for the N. Atlantic, its varibility, high and low, drives the annual results for this basin.  Note also the peaks in 2010, lows after 2014, and a rise in 2021. Now in 2023 the peak was holding at 1.4C before decling.  An annual chart below is informative:

Note the difference between blue/green years, beige/brown, and purple/red years.  2010, 2021, 2022 all peaked strongly in August or September.  1998 and 2007 were mildly warm.  2016 and 2018 were matching or cooler than the global average.  2023 started out slightly warm, then rose steadily to an  extraordinary peak in July.  August to October were only slightly lower, but by December cooled by ~0.4C. January 2024 is unchanged from the previous month.

The pattern suggests the ocean may be demonstrating a stairstep pattern like that we have also seen in HadCRUT4. 

The purple line is the average anomaly 1980-1996 inclusive, value 0.18.  The orange line the average 1980-202306, value 0.38, also for the period 1997-2012. The red line is 2013-202306, value 0.64. As noted above, these rising stages are driven by the combined warming in the Tropics and NH, including both Pacific and Atlantic basins.

Curiosity:  Solar Coincidence?

The news about our current solar cycle 25 is that the solar activity is hitting peak numbers now and higher  than expected 1-2 years in the future.  As livescience put it:  Solar maximum could hit us harder and sooner than we thought. How dangerous will the sun’s chaotic peak be?  Some charts from spaceweatherlive look familar to these sea surface temperature charts.

Summary

The oceans are driving the warming this century.  SSTs took a step up with the 1998 El Nino and have stayed there with help from the North Atlantic, and more recently the Pacific northern “Blob.”  The ocean surfaces are releasing a lot of energy, warming the air, but eventually will have a cooling effect.  The decline after 1937 was rapid by comparison, so one wonders: How long can the oceans keep this up? And is the sun adding forcing to this process?

Space weather impacts the ionosphere in this animation. Credits: NASA/GSFC/CIL/Krystofer Kim

Footnote: Why Rely on HadSST4

HadSST is distinguished from other SST products because HadCRU (Hadley Climatic Research Unit) does not engage in SST interpolation, i.e. infilling estimated anomalies into grid cells lacking sufficient sampling in a given month. From reading the documentation and from queries to Met Office, this is their procedure.

HadSST4 imports data from gridcells containing ocean, excluding land cells. From past records, they have calculated daily and monthly average readings for each grid cell for the period 1961 to 1990. Those temperatures form the baseline from which anomalies are calculated.

In a given month, each gridcell with sufficient sampling is averaged for the month and then the baseline value for that cell and that month is subtracted, resulting in the monthly anomaly for that cell. All cells with monthly anomalies are averaged to produce global, hemispheric and tropical anomalies for the month, based on the cells in those locations. For example, Tropics averages include ocean grid cells lying between latitudes 20N and 20S.

Gridcells lacking sufficient sampling that month are left out of the averaging, and the uncertainty from such missing data is estimated. IMO that is more reasonable than inventing data to infill. And it seems that the Global Drifter Array displayed in the top image is providing more uniform coverage of the oceans than in the past.

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean