Climate Science | Science Matters

Bill Gray: H20 is Climate Control Knob, not CO2

Posted on October 26, 2018 by Ron Clutz

William Mason Gray (1929-2016), pioneering hurricane scientist and forecaster and professor of atmospheric science at Colorado State University

Dr. William Gray made a compelling case for H2O as the climate thermostat, prior to his death in 2016. Thanks to GWPF for publishing posthumously Bill Gray’s understanding of global warming/climate change. The paper was compiled at his request, completed and now available as Flaws in applying greenhouse warming to Climate Variability This post provides some excerpts in italics with my bolds and some headers. Readers will learn much from the entire document (title above is link to pdf).

The Fundamental Correction

The critical argument that is made by many in the global climate modeling (GCM) community is that an increase in CO2 warming leads to an increase in atmospheric water vapor, resulting in more warming from the absorption of outgoing infrared radiation (IR) by the water vapor. Water vapor is the most potent greenhouse gas present in the atmosphere in large quantities. Its variability (i.e. global cloudiness) is not handled adequately in GCMs in my view. In contrast to the positive feedback between CO2 and water vapor predicted by the GCMs, it is my hypothesis that there is a negative feedback between CO2 warming and and water vapor. CO2 warming ultimately results in less water vapor (not more) in the upper troposphere. The GCMs therefore predict unrealistic warming of global temperature. I hypothesize that the Earth’s energy balance is regulated by precipitation (primarily via deep cumulonimbus (Cb) convection) and that this precipitation counteracts warming due to CO2.

Figure 14: Global surface temperature change since 1880. The dotted blue and dotted red lines illustrate how much error one would have made by extrapolating a multi-decadal cooling or warming trend beyond a typical 25-35 year period. Note the recent 1975-2000 warming trend has not continued, and the global temperature remained relatively constant until 2014.

Projected Climate Changes from Rising CO2 Not Observed

Continuous measurements of atmospheric CO2, which were first made at Mauna Loa, Hawaii in 1958, show that atmospheric concentrations of CO2 have risen since that time. The warming influence of CO2 increases with the natural logarithm (ln) of the atmosphere’s CO2 concentration. With CO2 concentrations now exceeding 400 parts per million by volume (ppm), the Earth’s atmosphere is slightly more than halfway to containing double the 280 ppm CO2 amounts in 1860 (at the beginning of the Industrial Revolution).∗

We have not observed the global climate change we would have expected to take place, given this increase in CO2. Assuming that there has been at least an average of 1 W/m2 CO2 blockage of IR energy to space over the last 50 years and that this energy imbalance has been allowed to independently accumulate and cause climate change over this period with no compensating response, it would have had the potential to bring about changes in any one of the following global conditions:

Warm the atmosphere by 180◦C if all CO2 energy gain was utilized for this purpose – actual warming over this period has been about 0.5◦C, or many hundreds of times less.
Warm the top 100 meters of the globe’s oceans by over 5◦C – actual warming over this period has been about 0.5◦C, or 10 or more times less.
Melt sufficient land-based snow and ice as to raise the global sea level by about 6.4 m. The actual rise has been about 8–9 cm, or 60–70 times less. The gradual rise of sea level has been only slightly greater over the last ~50 years (1965–2015) than it has been over the previous two ~50-year periods of 1915–1965 and 1865–1915, when atmospheric CO2 gain was much less.
Increase global rainfall over the past ~50-year period by 60 cm.

Earth Climate System Compensates for CO2

If CO2 gain is the only influence on climate variability, large and important counterbalancing influences must have occurred over the last 50 years in order to negate most of the climate change expected from CO2’s energy addition. Similarly, this hypothesized CO2-induced energy gain of 1 W/m2 over 50 years must have stimulated a compensating response that acted to largely negate energy gains from the increase in CO2.

The continuous balancing of global average in-and-out net radiation flux is therefore much larger than the radiation flux from anthropogenic CO2. For example, 342 W/m2, the total energy budget, is almost 100 times larger than the amount of radiation blockage expected from a CO2 doubling over 150 years. If all other factors are held constant, a doubling of CO2 requires a warming of the globe of about 1◦C to enhance outward IR flux by 3.7 W/m2 and thus balance the blockage of IR flux to space.

Figure 2: Vertical cross-section of the annual global energy budget. Determined from a combination of satellite-derived radiation measurements and reanalysis data over the period of 1984–2004.

This pure IR energy blocking by CO2 versus compensating temperature increase for radiation equilibrium is unrealistic for the long-term and slow CO2 increases that are occurring. Only half of the blockage of 3.7 W/m2 at the surface should be expected to go into an temperature increase. The other half (about 1.85 W/m2) of the blocked IR energy to space will be compensated by surface energy loss to support enhanced evaporation. This occurs in a similar way to how the Earth’s surface energy budget compensates for half its solar gain of 171 W/m2 by surface-to-air upward water vapor flux due to evaporation.

Assuming that the imposed extra CO2 doubling IR blockage of 3.7 W/m2 is taken up and balanced by the Earth’s surface in the same way as the solar absorption is taken up and balanced, we should expect a direct warming of only ~0.5◦C for a doubling of CO2. The 1◦C expected warming that is commonly accepted incorrectly assumes that all the absorbed IR goes to the balancing outward radiation with no energy going to evaporation.

Consensus Science Exaggerates Humidity and Temperature Effects

A major premise of the GCMs has been their application of the National Academy of Science (NAS) 1979 study3 – often referred to as the Charney Report – which hypothesized that a doubling of atmospheric CO2 would bring about a general warming of the globe’s mean temperature of 1.5–4.5◦C (or an average of ~3.0◦C). These large warming values were based on the report’s assumption that the relative humidity (RH) of the atmosphere remains quasiconstant as the globe’s temperature increases. This assumption was made without any type of cumulus convective cloud model and was based solely on the Clausius–Clapeyron (CC) equation and the assumption that the RH of the air will remain constant during any future CO2-induced temperature changes. If RH remains constant as atmospheric temperature increases, then the water vapor content in the atmosphere must rise exponentially.

With constant RH, the water vapor content of the atmosphere rises by about 50% if atmospheric temperature is increased by 5◦C. Upper tropospheric water vapor increases act to raise the atmosphere’s radiation emission level to a higher and thus colder level. This reduces the amount of outgoing IR energy which can escape to space by decreasing T^4.

These model predictions of large upper-level tropospheric moisture increases have persisted in the current generation of GCM forecasts.§ These models significantly overestimate globally-averaged tropospheric and lower stratospheric (0–50,000 feet) temperature trends since 1979 (Figure 7).

Figure 8: Decline in upper tropospheric RH. Annually-averaged 300 mb relative humidity for the tropics (30°S–30°N). From NASA-MERRA2 reanalysis for 1980–2016. Black dotted line is linear trend.

All of these early GCM simulations were destined to give unrealistically large upper-tropospheric water vapor increases for doubling of CO2 blockage of IR energy to space, and as a result large and unrealistic upper tropospheric temperature increases were predicted. In fact, if data from NASA-MERRA24 and NCEP/NCAR5 can be believed, upper tropospheric RH has actually been declining since 1980 as shown in Figure 8. The top part of Table 1 shows temperature and humidity differences between very wet and dry years in the tropics since 1948; in the wettest years, precipitation was 3.9% higher than in the driest ones. Clearly, when it rains more in the tropics, relative and specific humidity decrease. A similar decrease is seen when differencing 1995–2004 from 1985–1994, periods for which the equivalent precipitation difference is 2%. Such a decrease in RH would lead to a decrease in the height of the radiation emission level and an increase in IR to space.

The Earth’s natural thermostat – evaporation and precipitation

What has prevented this extra CO2-induced energy input of the last 50 years from being realized in more climate warming than has actually occurred? Why was there recently a pause in global warming, lasting for about 15 years? The compensating influence that prevents the predicted CO2-induced warming is enhanced global surface evaporation and increased precipitation.

Annual average global evaporational cooling is about 80 W/m2 or about 2.8 mm per day. A little more than 1% extra global average evaporation per year would amount to 1.3 cm per year or 65 cm of extra evaporation integrated over the last 50 years. This is the only way that such a CO2-induced , 1 W/m2 IR energy gain sustained over 50 years could occur without a significant alteration of globally-averaged surface temperature. This hypothesized increase in global surface evaporation as a response to CO2-forced energy gain should not be considered unusual. All geophysical systems attempt to adapt to imposed energy forcings by developing responses that counter the imposed action. In analysing the Earth’s radiation budget, it is incorrect to simply add or subtract energy sources or sinks to the global system and expect the resulting global temperatures to proportionally change. This is because the majority of CO2-induced energy gains will not go into warming the atmosphere. Various amounts of CO2-forced energy will go into ocean surface storage or into ocean energy gain for increased surface evaporation. Therefore a significant part of the CO2 buildup (~75%) will bring about the phase change of surface liquid water to atmospheric water vapour. The energy for this phase change must come from the surface water, with an expenditure of around 580 calories of energy for every gram of liquid that is converted into vapour. The surface water must thus undergo a cooling to accomplish this phase change.

Therefore, increases in anthropogenic CO2 have brought about a small (about 0.8%) speeding up of the globe’s hydrologic cycle, leading to more precipitation, and to relatively little global temperature increase. Therefore, greenhouse gases are indeed playing an important role in altering the globe’s climate, but they are doing so primarily by increasing the speed of the hydrologic cycle as opposed to increasing global temperature.

Figure 9: Two contrasting views of the effects of how the continuous intensification of deep
cumulus convection would act to alter radiation flux to space.
The top (bottom) diagram represents a net increase (decrease) in radiation to space

Tropical Clouds Energy Control Mechanism

It is my hypothesis that the increase in global precipitation primarily arises from an increase in deep tropical cumulonimbus (Cb) convection. The typical enhancement of rainfall and updraft motion in these areas together act to increase the return flow mass subsidence in the surrounding broader clear and partly cloudy regions. The upper diagram in Figure 9 illustrates the increasing extra mass flow return subsidence associated with increasing depth and intensity of cumulus convection. Rainfall increases typically cause an overall reduction of specific humidity (q) and relative humidity (RH) in the upper tropospheric levels of the broader scale surrounding convective subsidence regions. This leads to a net enhancement of radiation flux to space due to a lowering of the upper-level emission level. This viewpoint contrasts with the position in GCMs, which suggest that an increase in deep convection will increase upper-level water vapour.

Figure 10: Conceptual model of typical variations of IR, albedo and net (IR + albedo) associated with three different areas of rain and cloud for periods of increased precipitation.

The albedo enhancement over the cloud–rain areas tends to increase the net (IR + albedo) radiation energy to space more than the weak suppression of (IR + albedo) in the clear areas. Near-neutral conditions prevail in the partly cloudy areas. The bottom diagram of Figure 9 illustrates how, in GCMs, Cb convection erroneously increases upper tropospheric moisture. Based on reanalysis data (Table 1, Figure 8) this is not observed in the real atmosphere.

Ocean Overturning Circulation Drives Warming Last Century

A slowing down of the global ocean’s MOC is the likely cause of most of the global warming that has been observed since the latter part of the 19th century.15 I hypothesize that shorter multi-decadal changes in the MOC16 are responsible for the more recent global warming periods between 1910–1940 and 1975–1998 and the global warming hiatus periods between 1945–1975 and 2000–2013.

Figure 12: The effect of strong and weak Atlantic THC. Idealized portrayal of the primary Atlantic Ocean upper ocean currents during strong and weak phases of the thermohaline circulation (THC)

Figure 13 shows the circulation features that typically accompany periods when the MOC is stronger than normal and when it is weaker than normal. In general, a strong MOC is associated with a warmer-than-normal North Atlantic, increased Atlantic hurricane activity, increased blocking action in both the North Atlantic and North Pacific and weaker westerlies in the mid-latitude Southern Hemisphere. There is more upwelling of cold water in the South Pacific and Indian Oceans, and an increase in global rainfall of a few percent occurs. This causes the global surface temperatures to cool. The opposite occurs when the MOC is weaker than normal.

The average strength of the MOC over the last 150 years has likely been below the multimillennium average, and that is the primary reason we have seen this long-term global warming since the late 19th century. The globe appears to be rebounding from the conditions of the Little Ice Age to conditions that were typical of the earlier ‘Medieval’ and ‘Roman’ warm periods.

Summary and Conclusions

The Earth is covered with 71% liquid water. Over the ocean surface, sub-saturated winds blow, forcing continuous surface evaporation. Observations and energy budget analyses indicate that the surface of the globe is losing about 80 W/m2 of energy from the global surface evaporation process. This evaporation energy loss is needed as part of the process of balancing the surface’s absorption of large amounts of incoming solar energy. Variations in the strength of the globe’s hydrologic cycle are the way that the global climate is regulated. The stronger the hydrologic cycle, the more surface evaporation cooling occurs, and greater the globe’s IR flux to space. The globe’s surface cools when the hydrologic cycle is stronger than average and warms when the hydrologic cycle is weaker than normal. The strength of the hydrologic cycle is thus the primary regulator of the globe’s surface temperature. Variations in global precipitation are linked to long-term changes in the MOC (or THC).

I have proposed that any additional warming from an increase in CO2 added to the atmosphere is offset by an increase in surface evaporation and increased precipitation (an increase in the water cycle). My prediction seems to be supported by evidence of upper tropospheric drying since 1979 and the increase in global precipitation seen in reanalysis data. I have shown that the additional heating that may be caused by an increase in CO2 results in a drying, not a moistening, of the upper troposphere, resulting in an increase of outgoing radiation to space, not a decrease as proposed by the most recent application of the greenhouse theory.

Deficiencies in the ability of GCMs to adequately represent variations in global cloudiness, the water cycle, the carbon cycle, long-term changes in deep-ocean circulation, and other important mechanisms that control the climate reduce our confidence in the ability of these models to adequately forecast future global temperatures. It seems that the models do not correctly handle what happens to the added energy from CO2 IR blocking.

Figure 13: Effect of changes in MOC: top, strong MOC; bottom weak MOC. SLP: sea level pressure; SST, sea surface temperature.

Solar variations, sunspots, volcanic eruptions and cosmic ray changes are energy-wise too small to play a significant role in the large energy changes that occur during important multi-decadal and multi-century temperature changes. It is the Earth’s internal fluctuations that are the most important cause of climate and temperature change. These internal fluctuations are driven primarily by deep multi-decadal and multi-century ocean circulation changes, of which naturally varying upper-ocean salinity content is hypothesized to be the primary driving mechanism. Salinity controls ocean density at cold temperatures and at high latitudes where the potential deep-water formation sites of the THC and SAS are located. North Atlantic upper ocean salinity changes are brought about by both multi-decadal and multi-century induced North Atlantic salinity variability.

josh-knobs

Footnote:

The main point from Bill Gray was nicely summarized in a previous post Earth Climate Layers

The most fundamental of the many fatal mathematical flaws in the IPCC related modelling of atmospheric energy dynamics is to start with the impact of CO2 and assume water vapour as a dependent ‘forcing’. This has the tail trying to wag the dog. The impact of CO2 should be treated as a perturbation of the water cycle. When this is done, its effect is negligible. — Dr. Dai Davies

climate-onion2

2018 Update: Best Climate Model INMCM5

Posted on October 22, 2018 by Ron Clutz

Update February 4, 2020

A recent comparison of INMCM5 with other CMIP6 climate models is discussed at the post:
Climate Models: Good, Bad and Ugly

A previous analysis Temperatures According to Climate Models showed that only one of 42 CMIP5 models was close to hindcasting past temperature fluctuations. That model was INMCM4, which also projected an unalarming 1.4C warming to the end of the century, in contrast to the other models programmed for future warming five times the past.

In a recent comment thread, someone asked what has been done recently with that model, given that it appears to be “best of breed.” So I went looking and this post summarizes further work to produce a new, hopefully improved version by the modelers at the Institute of Numerical Mathematics of the Russian Academy of Sciences.

A previous post a year ago went into the details of improvements made in producing the latest iteration INMCM5 for entry into the CMIP6 project. That text is reprinted below. Now we have some initial and promising results Simulation of observed climate changes in 1850-2014 with climate model INM-CM5 published May 8, 2018 by Evgeny Volodin and Andrey Gritsun in Earth Systems Dynamics. Excerpts in italics with my bolds.

Figure 1. The 5-year mean GMST (K) anomaly with respect to 1850–1899 for HadCRUTv4 (thick solid black); model mean (thick solid red). Dashed thin lines represent data from individual model runs: 1 – purple, 2 – dark blue, 3 – blue, 4 – green, 5 – yellow, 6 – orange, 7 – magenta. In this and the next figures numbers on the time axis indicate the first year of the 5-year mean.

Abstract

Climate changes observed in 1850-2014 are modeled and studied on the basis of seven historical runs with the climate model INM-CM5 under the scenario proposed for Coupled Model Intercomparison Project, Phase 6 (CMIP6). In all runs global mean surface temperature rises by 0.8 K at the end of the experiment (2014) in agreement with the observations. Periods of fast warming in 1920-1940 and 1980-2000 as well as its slowdown in 1950-1975 and 2000-2014 are correctly reproduced by the ensemble mean. The notable change here with respect to the CMIP5 results is correct reproduction of the slowdown of global warming in 2000-2014 that we attribute to more accurate description of the Solar constant in CMIP6 protocol. The model is able to reproduce correct behavior of global mean temperature in 1980-2014 despite incorrect phases of the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation indices in the majority of experiments. The Arctic sea ice loss in recent decades is reasonably close to the observations just in one model run; the model underestimates Arctic sea ice loss by the factor 2.5. Spatial pattern of model mean surface temperature trend during the last 30 years looks close the one for the ERA Interim reanalysis. Model correctly estimates the magnitude of stratospheric cooling.

Additional Commentary

Observational data of GMST for 1850-2014 used for verification of model results were produced by HadCRUT4 (Morice et al 2012). Monthly mean sea surface temperature (SST) data ERSSTv4 (Huang et al 2015) are used for comparison of the AMO and PDO indices with that of the model. Data of Arctic sea ice extent for 1979-2014 derived from satellite observations are taken from Comiso and Nishio (2008). Stratospheric temperature trend and geographical distribution of near surface air temperature trend for 1979-2014 are calculated from ERA Interim reanalysis data (Dee et al 2011).

Keeping in mind the arguments that the GMST slowdown in the beginning of 21st 6 century could be due to the internal variability of the climate system let us look at the behavior of the AMO and PDO climate indices. Here we calculated the AMO index in the usual way, as the SST anomaly in Atlantic at latitudinal band 0N-60N minus anomaly of the GMST. Model and observed 5 year mean AMO index time series are presented in Fig.3. The well known oscillation with a period of 60-70 years can be clearly seen in the observations. Among the model runs, only one (dashed purple line) shows oscillation with a period of about 70 years, but without significant maximum near year 2000. In other model runs there is no distinct oscillation with a period of 60-70 years but period of 20-40 years prevails. As a result none of seven model trajectories reproduces behavior of observed AMO index after year 1950 (including its warm phase at the turn of the 20th and 21st centuries). One can conclude that anthropogenic forcing is unable to produce any significant impact on the AMO dynamics as its index averaged over 7 realization stays around zero within one sigma interval (0.08). Consequently, the AMO dynamics is controlled by internal variability of the climate system and cannot be predicted in historic experiments. On the other hand the model can correctly predict GMST changes in 1980-2014 having wrong phase of the AMO (blue, yellow, orange lines on Fig.1 and 3).

Conclusions

Seven historical runs for 1850-2014 with the climate model INM-CM5 were analyzed. It is shown that magnitude of the GMST rise in model runs agrees with the estimate based on the observations. All model runs reproduce stabilization of GMST in 1950-1970, fast warming in 1980-2000 and a second GMST stabilization in 2000-2014 suggesting that the major factor for predicting GMST evolution is the external forcing rather than system internal variability. Numerical experiments with the previous model version (INMCM4) for CMIP5 showed unrealistic gradual warming in 1950-2014. The difference between the two model results could be explained by more accurate modeling of stratospheric volcanic and tropospheric anthropogenic aerosol radiation effect (stabilization in 1950-1970) due to the new aerosol block in INM-CM5 and more accurate prescription of Solar constant scenario (stabilization in 2000-2014) in CMIP6 protocol. Four of seven INM-CM5 model runs simulate acceleration of warming in 1920-1940 in a correct way, other three produce it earlier or later than in reality. This indicates that for the year warming of 1920-1940 the climate system natural variability plays significant role. No model trajectory reproduces correct time behavior of AMO and PDO indices. Taking into account our results on the GMST modeling one can conclude that anthropogenic forcing does not produce any significant impact on the dynamics of AMO and PDO indices, at least for the INM-CM5 model. In turns, correct prediction of the GMST changes in the 1980-2014 does not require correct phases of the AMO and PDO as all model runs have correct values of the GMST while in at least three model experiments the phases of the AMO and PDO are opposite to the observed ones in that time. The North Atlantic SST time series produced by the model correlates better with the observations in 1980-2014. Three out of seven trajectories have strongly positive North Atlantic SST anomaly as the observations (in the other four cases we see near-to-zero changes for this quantity). The INMCM5 has the same skill for prediction of the Arctic sea ice extent in 2000-2014 as CMIP5 models including INMCM4. It underestimates the rate of sea ice loss by a factor between the two and three. In one extreme case the magnitude of this decrease is as large as in the observations while in the other the sea ice extent does not change compared to the preindustrial ages. In part this could be explained by the strong internal variability of the Arctic sea ice but obviously the new version of INMCM model and new CMIP6 forcing protocol does not improve prediction of the Arctic sea ice extent response to anthropogenic forcing.

Previous Post: Climate Model Upgraded: INMCM5 Under the Hood

Earlier in 2017 came this publication Simulation of the present-day climate with the climate model INMCM5 by E.M. Volodin et al. Excerpts below with my bolds.

In this paper we present the ﬁfth generation of the INMCM climate model that is being developed at the Institute of Numerical Mathematics of the Russian Academy of Sciences (INMCM5). The most important changes with respect to the previous version (INMCM4) were made in the atmospheric component of the model. Its vertical resolution was increased to resolve the upper stratosphere and the lower mesosphere. A more sophisticated parameterization of condensation and cloudiness formation was introduced as well. An aerosol module was incorporated into the model. The upgraded oceanic component has a modiﬁed dynamical core optimized for better implementation on parallel computers and has two times higher resolution in both horizontal directions.

Analysis of the present-day climatology of the INMCM5 (based on the data of historical run for 1979–2005) shows moderate improvements in reproduction of basic circulation characteristics with respect to the previous version. Biases in the near-surface temperature and precipitation are slightly reduced compared with INMCM4 as well as biases in oceanic temperature, salinity and sea surface height. The most notable improvement over INMCM4 is the capability of the new model to reproduce the equatorial stratospheric quasi-biannual oscillation and statistics of sudden stratospheric warmings.

The family of INMCM climate models, as most climate system models, consists of two main blocks: the atmosphere general circulation model, and the ocean general circulation model. The atmospheric part is based on the standard set of hydrothermodynamic equations with hydrostatic approximation written in advective form. The model prognostic variables are wind horizontal components, temperature, speciﬁc humidity and surface pressure.

Atmosphere Module

The INMCM5 borrows most of the atmospheric parameterizations from its previous version. One of the few notable changes is the new parameterization of clouds and large-scale condensation. In the INMCM5 cloud area and cloud water are computed prognostically according to Tiedtke (1993). That includes the formation of large-scale cloudiness as well as the formation of clouds in the atmospheric boundary layer and clouds of deep convection. Decrease of cloudiness due to mixing with unsaturated environment and precipitation formation are also taken into account. Evaporation of precipitation is implemented according to Kessler (1969).

In the INMCM5 the atmospheric model is complemented by the interactive aerosol block, which is absent in the INMCM4. Concentrations of coarse and ﬁne sea salt, coarse and ﬁne mineral dust, SO2, sulfate aerosol, hydrophilic and hydrophobic black and organic carbon are all calculated prognostically.

Ocean Module

The oceanic module of the INMCM5 uses generalized spherical coordinates. The model “South Pole” coincides with the geographical one, while the model “North Pole” is located in Siberia beyond the ocean area to avoid numerical problems near the pole. Vertical sigma-coordinate is used. The ﬁnite-diﬀerence equations are written using the Arakawa C-grid. The diﬀerential and ﬁnite-diﬀerence equations, as well as methods of solving them can be found in Zalesny etal. (2010).

The INMCM5 uses explicit schemes for advection, while the INMCM4 used schemes based on splitting upon coordinates. Also, the iterative method for solving linear shallow water equation systems is used in the INMCM5 rather than direct method used in the INMCM4. The two previous changes were made to improve model parallel scalability. The horizontal resolution of the ocean part of the INMCM5 is 0.5 × 0.25° in longitude and latitude (compared to the INMCM4’s 1 × 0.5°).

Both the INMCM4 and the INMCM5 have 40 levels in vertical. The parallel implementation of the ocean model can be found in (Terekhov etal. 2011). The oceanic block includes vertical mixing and isopycnal diﬀusion parameterizations (Zalesny et al. 2010). Sea ice dynamics and thermodynamics are parameterized according to Iakovlev (2009). Assumptions of elastic-viscous-plastic rheology and single ice thickness gradation are used. The time step in the oceanic block of the INMCM5 is 15 min.

Note the size of the human emissions next to the red arrow.

Carbon Cycle Module

The climate model INMCM5 has а carbon cycle module (Volodin 2007), where atmospheric CO2 concentration, carbon in vegetation, soil and ocean are calculated. In soil, а single carbon pool is considered. In the ocean, the only prognostic variable in the carbon cycle is total inorganic carbon. Biological pump is prescribed. The model calculates methane emission from wetlands and has a simpliﬁed methane cycle (Volodin 2008). Parameterizations of some electrical phenomena, including calculation of ionospheric potential and ﬂash intensity (Mareev and Volodin 2014), are also included in the model.

Surface Temperatures

When compared to the INMCM4 surface temperature climatology, the INMCM5 shows several improvements. Negative bias over continents is reduced mainly because of the increase in daily minimum temperature over land, which is achieved by tuning the surface ﬂux parameterization. In addition, positive bias over southern Europe and eastern USA in summer typical for many climate models (Mueller and Seneviratne 2014) is almost absent in the INMCM5. A possible reason for this bias in many models is the shortage of soil water and suppressed evaporation leading to overestimation of the surface temperature. In the INMCM5 this problem was addressed by the increase of the minimum leaf resistance for some vegetation types.

Nevertheless, some problems migrate from one model version to the other: negative bias over most of the subtropical and tropical oceans, and positive bias over the Atlantic to the east of the USA and Canada. Root mean square (RMS) error of annual mean near surface temperature was reduced from 2.48 K in the INMCM4 to 1.85 K in the INMCM5.

Precipitation

In mid-latitudes, the positive precipitation bias over the ocean prevails in winter while negative bias occurs in summer. Compared to the INMCM4, the biases over the western Indian Ocean, Indonesia, the eastern tropical Paciﬁc and the tropical Atlantic are reduced. A possible reason for this is the better reproduction of the tropical sea surface temperature (SST) in the INMCM5 due to the increase of the spatial resolution in the oceanic block, as well as the new condensation scheme. RMS annual mean model bias for precipitation is 1.35mm day−1 for the INMCM5 compared to 1.60mm day−1 for the INMCM4.

Cloud Radiation Forcing

Cloud radiation forcing (CRF) at the top of the atmosphere is one of the most important climate model characteristics, as errors in CRF frequently lead to an incorrect surface temperature.

In the high latitudes model errors in shortwave CRF are small. The model underestimates longwave CRF in the subtropics but overestimates it in the high latitudes. Errors in longwave CRF in the tropics tend to partially compensate errors in shortwave CRF. Both errors have positive sign near 60S leading to warm bias in the surface temperature here. As a result, we have some underestimation of the net CRF absolute value at almost all latitudes except the tropics. Additional experiments with tuned conversion of cloud water (ice) to precipitation (for upper cloudiness) showed that model bias in the net CRF could be reduced, but that the RMS bias for the surface temperature will increase in this case.

A table from another paper provides the climate parameters described by INMCM5.

Climate Parameters	Observations	INMCM3	INMCM4	INMCM5
Incoming solar radiation at TOA	341.3 [26]	341.7	341.8	341.4
Outgoing solar radiation at TOA	96–100 [26]	97.5 ± 0.1	96.2 ± 0.1	98.5 ± 0.2
Outgoing longwave radiation at TOA	236–242 [26]	240.8 ± 0.1	244.6 ± 0.1	241.6 ± 0.2
Solar radiation absorbed by surface	154–166 [26]	166.7 ± 0.2	166.7 ± 0.2	169.0 ± 0.3
Solar radiation reflected by surface	22–26 [26]	29.4 ± 0.1	30.6 ± 0.1	30.8 ± 0.1
Longwave radiation balance at surface	–54 to 58 [26]	–52.1 ± 0.1	–49.5 ± 0.1	–63.0 ± 0.2
Solar radiation reflected by atmosphere	74–78 [26]	68.1 ± 0.1	66.7 ± 0.1	67.8 ± 0.1
Solar radiation absorbed by atmosphere	74–91 [26]	77.4 ± 0.1	78.9 ± 0.1	81.9 ± 0.1
Direct hear flux from surface	15–25 [26]	27.6 ± 0.2	28.2 ± 0.2	18.8 ± 0.1
Latent heat flux from surface	70–85 [26]	86.3 ± 0.3	90.5 ± 0.3	86.1 ± 0.3
Cloud amount, %	64–75 [27]	64.2 ± 0.1	63.3 ± 0.1	69 ± 0.2
Solar radiation-cloud forcing at TOA	–47 [26]	–42.3 ± 0.1	–40.3 ± 0.1	–40.4 ± 0.1
Longwave radiation-cloud forcing at TOA	26 [26]	22.3 ± 0.1	21.2 ± 0.1	24.6 ± 0.1
Near-surface air temperature, °С	14.0 ± 0.2 [26]	13.0 ± 0.1	13.7 ± 0.1	13.8 ± 0.1
Precipitation, mm/day	2.5–2.8 [23]	2.97 ± 0.01	3.13 ± 0.01	2.97 ± 0.01
River water inflow to the World Ocean,10^3 km^3/year	29–40 [28]	21.6 ± 0.1	31.8 ± 0.1	40.0 ± 0.3
Snow coverage in Feb., mil. Km^2	46 ± 2 [29]	37.6 ± 1.8	39.9 ± 1.5	39.4 ± 1.5
Permafrost area, mil. Km^2	10.7–22.8 [30]	8.2 ± 0.6	16.1 ± 0.4	5.0 ± 0.5
Land area prone to seasonal freezing in NH, mil. Km^2	54.4 ± 0.7 [31]	46.1 ± 1.1	48.3 ± 1.1	51.6 ± 1.0
Sea ice area in NH in March, mil. Km^2	13.9 ± 0.4 [32]	12.9 ± 0.3	14.4 ± 0.3	14.5 ± 0.3
Sea ice area in NH in Sept., mil. Km^2	5.3 ± 0.6 [32]	4.5 ± 0.5	4.5 ± 0.5	6.1 ± 0.5

Heat flux units are given in W/m^2; the other units are given with the title of corresponding parameter. Where possible, ± shows standard deviation for annual mean value. Source: Simulation of Modern Climate with the New Version Of the INM RAS Climate Model (Bracketed numbers refer to sources for observations)

Ocean Temperature and Salinity

The model biases in potential temperature and salinity averaged over longitude with respect to WOA09 (Antonov et al. 2010) are shown in Fig.12. Positive bias in the Southern Ocean penetrates from the surface downward for up to 300 m, while negative bias in the tropics can be seen even in the 100–1000 m layer.

Nevertheless, zonal mean temperature error at any level from the surface to the bottom is small. This was not the case for the INMCM4, where one could see negative temperature bias up to 2–3 K from 1.5 km to the bottom nearly al all latitudes, and 2–3 K positive bias at levels of 700–1000 m. The reason for this improvement is the introduction of a higher background coeﬃcient for vertical diﬀusion at high depth (3000 m and higher) than at intermediate depth (300–500m). Positive temperature bias at 45–65 N at all depths could probably be explained by shortcomings in the representation of deep convection [similar errors can be seen for most of the CMIP5 models (Flato etal. 2013, their Fig.9.13)].

Another feature common for many present day climate models (and for the INMCM5 as well) is negative bias in southern tropical ocean salinity from the surface to 500 m. It can be explained by overestimation of precipitation at the southern branch of the Inter Tropical Convergence zone. Meridional heat ﬂux in the ocean (Fig.13) is not far from available estimates (Trenberth and Caron 2001). It looks similar to the one for the INMCM4, but maximum of northward transport in the Atlantic in the INMCM5 is about 0.1–0.2 × 1015 W higher than the one in the INMCM4, probably, because of the increased horizontal resolution in the oceanic block.

Sea Ice

In the Arctic, the model sea ice area is just slightly overestimated. Overestimation of the Arctic sea ice area is connected with negative bias in the surface temperature. In the same time, connection of the sea ice area error with the positive salinity bias is not evident because ice formation is almost compensated by ice melting, and the total salinity source for these pair of processes is not large. The amplitude and phase of the sea ice annual cycle are reproduced correctly by the model. In the Antarctic, sea ice area is underestimated by a factor of 1.5 in all seasons, apparently due to the positive temperature bias. Note that the correct simulation of sea ice area dynamics in both hemispheres simultaneously is a diﬃcult task for climate modeling.

The analysis of the model time series of the SST anomalies shows that the El Niño event frequency is approximately the same in the model and data, but the model El Niños happen too regularly. Atmospheric response to the El Niño vents is also underestimated in the model by a factor of 1.5 with respect to the reanalysis data.

Conclusion

Based on the CMIP5 model INMCM4 the next version of the Institute of Numerical Mathematics RAS climate model was developed (INMCM5). The most important changes include new parameterizations of large scale condensation (cloud fraction and cloud water are now the prognostic variables), and increased vertical resolution in the atmosphere (73 vertical levels instead of 21, top model level raised from 30 to 60 km). In the oceanic block, horizontal resolution was increased by a factor of 2 in both directions.

The climate model was supplemented by the aerosol block. The model got a new parallel code with improved computational eﬃciency and scalability. With the new version of climate model we performed a test model run (80 years) to simulate the present-day Earth climate. The model mean state was compared with the available datasets. The structures of the surface temperature and precipitation biases in the INMCM5 are typical for the present climate models. Nevertheless, the RMS error in surface temperature, precipitation as well as zonal mean temperature and zonal wind are reduced in the INMCM5 with respect to its previous version, the INMCM4.

The model is capable of reproducing equatorial stratospheric QBO and SSWs.The model biases for the sea surface height and surface salinity are reduced in the new version as well, probably due to increasing spatial resolution in the oceanic block. Bias in ocean potential temperature at depths below 700 m in the INMCM5 is also reduced with respect to the one in the INMCM4. This is likely because of the tuning background vertical diﬀusion coeﬃcient.

Model sea ice area is reproduced well enough in the Arctic, but is underestimated in the Antarctic (as a result of the overestimated surface temperature). RMS error in the surface salinity is reduced almost everywhere compared to the previous model except the Arctic (where the positive bias becomes larger). As a ﬁnal remark one can conclude that the INMCM5 is substantially better in almost all aspects than its previous version and we plan to use this model as a core component for the coming CMIP6 experiment.

Summary

One the one hand, this model example shows that the intent is simple: To represent dynamically the energy balance of our planetary climate system. On the other hand, the model description shows how many parameters are involved, and the complexity of processes interacting. The attempt to simulate operations of the climate system is a monumental task with many outstanding challenges, and this latest version is another step in an iterative development.

Note: Regarding the influence of rising CO2 on the energy balance. Global warming advocates estimate a CO2 perturbation of 4 W/m^2. In the climate parameters table above, observations of the radiation fluxes have a 2 W/m^2 error range at best, and in several cases are observed in ranges of 10 to 15 W/m^2.

We do not yet have access to the time series temperature outputs from INMCM5 to compare with observations or with other CMIP6 models. Presumably that will happen in the future.

Early Schematic: Flows and Feedbacks for Climate Models

Ocean SSTs Slightly Cooler

Posted on October 17, 2018 by Ron Clutz

globpop_countries 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;
A major El Nino was 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, the latest version being HadSST3. More on what distinguishes HadSST3 from other SST products at the end.

The Current Context

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through September 2018

Hadsst092018

A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016. 2018 started with slow warming after the low point of December 2017, led by steadily rising NH. Since 4/2018 SH and Tropics cooled slightly in the Spring and NH dipped in July 2018.

In August all ocean regions bumped upward, and in September the Global average dipped slightly. Note that NH rose, unlike dips in the last two years, but is still 0.2C lower than 9/2015. The rise in the Tropics is likely due to the weak El Nino, since the SH dropped almost 0.1C, nearly matching the SH low for this period in 9/2017. That SH cooling is offsetting the NH rise.

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back below its beginning level. Secondly, the Northern Hemisphere added three bumps on the shoulders of Tropical warming, with peaks in August of each year. Also, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one.

A longer view of SSTs

The graph below 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.

Open image in new tab to enlarge.

1995 is a reasonable 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. For the next 2 years, the Tropics stayed down, and the world’s oceans held steady around 0.2C above 1961 to 1990 average.

Then comes a steady rise over two years to a lesser peak Jan. 2003, but again uniformly pulling all oceans up around 0.4C. Something changes at this point, with more hemispheric divergence than before. Over the 4 years until Jan 2007, the Tropics go through ups and downs, NH a series of ups and SH mostly downs. As a result the Global average fluctuates around that same 0.4C, which also turns out to be the average for the entire record since 1995.

2007 stands out with a sharp drop in temperatures so that Jan.08 matches the low in Jan. ’99, but starting from a lower high. The oceans all decline as well, until temps build peaking in 2010.

Now again a different pattern appears. The Tropics cool 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, with July 2017 only slightly lower. Note also that starting in 2014 SH plays a moderating role, offsetting the NH warming pulses. (Note: these are high anomalies on top of the highest absolute temps in the NH.)

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.

But the peaks coming nearly every summer in HadSST require a different picture. Let’s look at August, the hottest month in the North Atlantic from the Kaplan dataset.
AMO August 2018

The AMO Index is from from Kaplan SST v2, the unaltered and untrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N. The graph shows warming began after 1992 up to 1998, with a series of matching years since. Because the N. Atlantic has partnered with the Pacific ENSO recently, let’s take a closer look at some AMO years in the last 2 decades.

AMO decade 092018

This graph shows monthly AMO temps for some important years. The Peak years were 1998, 2010 and 2016, with the latter emphasized as the most recent. The other years show lesser warming, with 2007 emphasized as the coolest in the last 20 years. Note the red 2018 line is at the bottom of all these tracks. Most recently September 2018 is 0.29C lower than September 2016, and is the coolest September since 2011.

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? If the pattern of recent years continues, NH SST anomalies will likely cool in coming months. Once again, ENSO will probably determine the outcome.

Postscript:

In the most recent GWPF 2017 State of the Climate report, Dr. Humlum made this observation:

“It is instructive to consider the variation of the annual change rate of atmospheric CO2 together with the annual change rates for the global air temperature and global sea surface temperature (Figure 16). All three change rates clearly vary in concert, but with sea surface temperature rates leading the global temperature rates by a few months and atmospheric CO2 rates lagging 11–12 months behind the sea surface temperature rates.”

Footnote: Why Rely on HadSST3

HadSST3 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.

HadSST3 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

Climate Tipping Points Quiz

Posted on October 12, 2018 by Ron Clutz

This post is a reblog of the Manhattan Contrarian Quiz — Climate Tipping Points Edition
October 11, 2018/ by Francis Menton. Text in italics with my bolds.

On Monday the UN IPCC came out with its latest Special Report, this one supposedly addressed specifically to the allegedly dire consequences of allowing world temperatures to increase by more than an arbitrarily-selected threshold. Here is a copy of the “Summary for Policymakers,” and here is a copy of the accompanying press release. But I urge you not to peek at those until you have taken today’s very important Manhattan Contrarian Climate Tipping Points Quiz.

Many have noted that this latest Report seems to step up the level of hysteria and shrieking about the threat of climate change to a whole new level. The gist is, we are doomed, doomed, doomed unless mankind takes immediate drastic action to reduce and then eliminate carbon emissions, because otherwise we will shortly cross the dreaded climate “tipping point.” Crossing the tipping point means that climate change will thereafter accelerate out of control, there will be no further chance of saving the planet, and all hope must be abandoned. You can see that this is very serious, at least if you give any credence to this stuff. And yet, despite the hyperbole, this report seems to be getting much less attention than prior similar predictions of the impending climate apocalypse, even if no one in the mainstream press will apply the slightest amount of critical thinking as to whether any of this makes any sense at all. As an example, the big New York Times article on the Report did not appear until Tuesday, and in the print edition ran on page A8. I guess there were plenty of things more important than the approaching end of the world to fill up the front page.

So it’s time to take the Manhattan Contrarian Climate Tipping Points Quiz. The quiz consists of nine predictions of the impending climate “tipping point,” made at various points over the past few decades. For each prediction, I have deleted the name of the predictor, the year made and the year or years that were identified as the dreaded tipping point, but have included in brackets the number of years in the future that the tipping point was said to be at the time of the prediction in question. Your task is to identify which of the predictions is the one found in the current UN materials. For extra credit, see if you can identify any of the other predictions as to the person or organization uttering the prediction, the year made, and the year said to be the date of the tipping point.

Answers below the fold.

Prediction Number 1:

[Predictor] said that without “coherent financial incentives and disincentives” we have just 96 months to avert “irretrievable climate and ecosystem collapse, and all that goes with it.” . . . He confided last night: “We face the dual challenges of a world view and an economic system that seem to have enormous shortcomings, together with an environmental crisis – including that of climate change – which threatens to engulf us all.”

Prediction Number 2:

[U]nless drastic measures to reduce greenhouse gases are taken within the next 10 years, the world will reach a point of no return, [predictor] said. He sees the situation as “a true planetary emergency.” “If you accept the truth of that, then nothing else really matters that much,” [predictor] said in an interview with The Associated Press. “We have to organize quickly to come up with a coherent and really strong response, and that’s what I’m devoting myself to.”

Prediction Number 3:

[Predictor] . . . told author Bob Reiss in [year of prediction] that New York City would be underwater in 20 years. “The West Side Highway [which runs along the Hudson River] will be under water,” [predictor] said. “And there will be tape across the windows across the street because of high winds. And the same birds won’t be there. The trees in the median strip will change.”

Prediction Number 4:

The year: [46 years after prediction]. Massive dikes around New Orleans, Miami, and New York are holding back rising sea water. Phoenix is baking in its third straight week of temperatures above 115 degrees. Decades of drought have laid waste to the once-fertile Midwestern farm belt. Hurricanes batter the Gulf Coast, and forest fires continue to black thousands of acres across the country. Science fiction? Hardly. These are the sobering global warming or “greenhouse effect” scenarios that many scientists believe may happen if we continue to pollute our environment. . . . [N]othing short of an immediate worldwide effort by governments, corporations and especially individual citizens will be needed to reverse the environmental crisis that now threatens the entire planet.

Prediction Number 5:

In [year of prediction], [predictor] told [publication] that [4 years after prediction] was “the last window of opportunity” to impose policies to restrict fossil fuel use. [Predictor] said it’s “the last chance we have to get anything approaching [numeric] degrees Centigrade,” adding that if “we don’t do it now, we are committing the world to a drastically different place.”

Prediction Number 6:

[W]arming of [numeric] deg C or higher increases the risk associated with long-lasting or irreversible changes, such as the loss of some ecosystems,” said [predictor]. . . . [L]imiting global warming to [numeric]°C would require “rapid and far-reaching” transitions in land, energy, industry, buildings, transport, and cities. Global net human-caused emissions of carbon dioxide (CO2) would need to fall by about 45 percent from [year] levels by [12 years from prediction], reaching ‘net zero’ around [32 years from prediction].

Prediction Number 7:

[Predictor] said in [year of prediction] that if “there’s no action before [5 years after prediction], that’s too late.” “What we do in the next two to three years will determine our future. This is the defining moment,” he said.

Prediction Number 8:

[Predictor] wrote in [publication] that within “as little as 10 years, the world will be faced with a choice: arable farming either continues to feed the world’s animals or it continues to feed the world’s people. It cannot do both.”

Prediction Number 9:

[Publication] reported in [year] that [predictor] says entire nations could be wiped off the face of the earth by rising sea levels if global warming is not reversed by the [year].”

Answers to Quiz:

Answer for Prediction Number 1: This famous prediction was made by the great climate scientist Prince Charles in July 2009. The 96 month (8 year) period of the prediction expired in July 2017. Does that mean that for a year plus we have already been in the state of “irretrievable climate and ecosystem collapse”?

Answer for Prediction Number 2: Again, this is a quite famous prediction, made by Al Gore at the January 2006 premier of his climate apocalypse movie “An Inconvenient Truth,” as reported at the time by CBS News. Thus, the period of the prediction expired in January 2016. I guess then that we have already reached the “point of no return” and the “true planetary emergency.” How does it feel?

Answer for Prediction Number 3: Another famous prediction, this one made in 1988 by James Hansen, then head of the branch of NASA known as GISS that collects (and fraudulently alters) world temperature data. This time the 20 year prediction period expired in 2008. Meanwhile, I went down the West Side Highway just a few days ago, and the water didn’t appear any closer to swamping it than it was back in 1988.

Answer for Prediction Number 4: This one comes from self-described all-around genius Jeremy Rifkin (“author of 20 bestselling books about the impact of scientific and technological changes on the economy, the workforce, society, and the environment.” and “advisor to the leadership of the European Union since 2000” — really, does that tell you all you need to know about what idiots the Europeans are?), and is found in an article in none other than the Poughkeepsie Journal (my hometown newspaper!) in 1989. OK, the date for the prediction (2035) hasn’t arrived yet. But, if we were going to need “massive dikes” to protect New York City by 2035, shouldn’t there be by now some evidence of the sea level going up?

Answer for Prediction Number 5: The predictor was then-head of the United Nations Foundation Timothy Wirth, and the year of prediction was 2012. That means that the date for the prediction was 2016 — or actually, in the phrasing of the prediction, the end of President Obama’s second term. The prediction appeared in ClimateWire. I guess we missed our “last chance” to save the world. Wirth is the same guy who, as a Congressman back in 1988, promoted the hearings featuring Hansen that many credit as the official launch of the global warming scare.

Answer for Prediction Number 6: Yes, this quote comes from the just-issued press release announcing the new UN Report. There were enough extraneous clues in there that probably many of you readers got it right. The number of degrees C that is said in this Report to be the brink of disaster is 1.5.

Answer for Prediction Number 7: The predictor is former UN IPCC head Rajendra Pachauri, and the year of prediction was 2007. That means that after 2012 it was “too late” to stop armageddon. Oh, well. Somehow we struggle on.

Answer for Prediction Number 8: This one comes from noted UK environmental writer George Monbiot, and appeared in the Guardian in 2002. So once again the year for the prediction was 2012. Do you recall the world making the choice somewhere 6 or so years ago between “feeding the world’s animals” and “feeding the world’s people.” I’m struggling to remember that. Perhaps I should go home and have a hamburger for dinner while I think it over.

Answer for Prediction Number 9: The prediction comes from 1989, and the year for the prediction (“entire nations . . . wiped off the face of the earth”) was 2000 — 18 years ago. The publication was the San Jose Mercury News, which attributed the prediction to UN “senior environmental official” Noel Brown. Somehow, even the Maldives seem to be doing fine here in 2018.

Here’s the incredible thing: Wouldn’t you think that making apocalyptic predictions like these that failed so completely would undermine the predictors’ reputations somehow — like maybe, they’d be considered laughingstocks? Not at all! All of these guys are still out there and going strong. OK, Pachauri was forced out of the IPCC, but over sexual harassment allegations, not failed climate predictions. He left the IPCC in 2015, which means that three years after his prediction above bombed, he was still there. Meanwhile, the IPCC had won the Nobel Peace Prize! Monbiot still writes climate doom articles for the Guardian. And you haven’t heard of Noel Brown? He retired from the UNDP, but has gone on to be President of the Friends of the United Nations.

Yes, ridiculous failed climate apocalypse predictions are the route to assured career success. The world is a funny place.

Update October 13, 2018

An interesting essay by Sean Gabb (H/T Greenie Watch) provides some additional predictions justifying our skepticism about environmentalists’ doomsday narrative. The Environmental Scam: One Quick and Easy Response Excerpt in italics: The entire article is informative.

Sean Gabb writes:

I now turn to the claims about global warming. I will not discuss the intricacies of how much carbon dioxide we are releasing, or what effect this may have on temperatures. I leave aside the persistent claims of scientific fraud and other corruption. As said, I am not qualified to comment on these or other matters. What I do note is that, in 2006, Al Gore

[p]atiently, and surely for the 10,000th time, [explained to The Guardian] what’s going wrong. The atmosphere is like a coat of varnish around the globe, he says. When it’s thin, as it should be, heat naturally escapes. But when it gets thicker, thanks to carbon dioxide emitted by us, it traps in the heat and the world gets warmer. “It’s cooking and wilting the most vulnerable parts of the eco-system, melting all the mountain glaciers, the north polar ice cap, parts of Antarctica, parts of Greenland.” That molten ice-water will raise sea-levels, flooding food-producing areas that all of us rely on. Eventually it will submerge whole cities, from San Francisco to Shanghai. The site of the Twin Towers will not be a memorial garden: it will be underwater.

… He agrees with the scientists who say we have 10 years to act, before we cross a point of no return.

In 2009, the Prince of Wales – advised by the “leading environmentalists Jonathon Porritt and Tony Juniper” – said we had 96 months to change our ways. After that, we faced “irretrievable climate and ecosystem collapse, and all that goes with it.”

In 2005, George Monbiot wrote in The Guardian:

Winter is no longer the great grey longing of my childhood. The freezes this country suffered in 1982 and 1963 are – unless the Gulf Stream stops – unlikely to recur. Our summers will be long and warm. Across most of the upper northern hemisphere, climate change, so far, has been kind to us.

Ten years took us to 2016. Assuming my arithmetic is correct, 96 months take us to about now. If we have really reached the “point of no return,” why have these people not yet switched to telling us “I warned you: now it’s too late”? Instead, the apocalyptic warnings continue at top volume. Oh – and English weather remains as unpredictable today as it was in 2005. In March this year, there was an inch of snow in Deal.

The point of repeating these claims is that they were not random assertions, but appear to have been made on scientific advice – scientific advice that turned out to be wrong. Whether the scientists in question were lying, or whether they advised in good faith, is less important than that they were wrong. You do not need a degree in the natural sciences to notice when predictions are falsified. It is with this in mind that I take the present claims of plastic waste in the sea, and reject them out of hand. It may be that, this time, the claims are true. But the whole burden of proof is on those making them. The burden of proof comes with the barely-rebuttable presumption that we are being fed yet another diet of alarmist falsehoods.

Dr. Indrani Roy on Natural Climate Factors

Posted on October 12, 2018 by Ron Clutz

Figure 1. Total solar irradiance over the past three solar cycles, since 1975, varying between 1365 and 1367 W/m2. Source: NASA

A new paper by Dr. Indrani Roy is Abrupt Global Warming, Warming Trend Slowdown and Related Features in Recent Decades. The thrust of her conclusions is reported in this Science Daily article Role of ‘natural factors’ on recent climate change underestimated, research shows Excerpts in italics with my bolds.

Pioneering new research has given a new perspective on the crucial role that ‘natural factors’ play in global warming.

The study, by Dr Indrani Roy at the University of Exeter, suggests that the natural phenomena such as solar eleven-year cycles and strong volcanic explosions play important roles in recent climate change which has been ‘underestimated’.

All existing studies focus on the rise in CO2 in the atmosphere as being the main driver of global temperature rises.

However, Dr Roy suggests that the role natural factors plays in climate change should be given more prominence. This study explores various possible areas where models miss important contributions due to these natural drivers.

The research is published in leading journal Frontiers.

Although CO2 has risen significantly since 1998, global temperature did not show any significant increase. Models however suggested a significant rise.

Dr Roy said: “So what factors are missing? It is a puzzle of recent slowdown of global warming trend or Hiatus and this study addresses that issue.”

For the study, Dr Roy looked specifically at data between 1976-96, which not only covered two full strong solar cycles and two explosive volcanic eruptions during active phases of those cycles, but which also matched a period of abrupt global warming. These data were compared with other periods.

The research highlighted the important role that a dominant Central Pacific (CP) El Nino, and its associated water vapour feedback, played in global warming within the chosen period.

Plinian column of the eruption of Sarychev (Russia) on 12 June 2009. Credit: NASA

Dr Roy suggests that the explosive volcanoes seen during this phase, which changed the sea level pressure around the North Atlantic, kick-started a ‘chain mechanism’ that played a crucial role.

Dr Roy added that the change in Indian Summer Monsoons and El Nino connection during that abrupt warming period, and a subsequent recovery thereafter, can also be explained by this ‘chain mechanism’.

Discussion of Chain Mechanism from Frontiers paper

The puzzle of global warming hiatus is discussed in many recent studies, though the underlying cause is still unexplained. Many climate features, in atmosphere and ocean including global temperature trend, suffered deviations during later two decades of the last century, so as some known teleconnection patterns. This study addresses those areas segregating the role of natural factors (the sun and volcano) to that from CO2 led linear anthropogenic influences. To analyse the combined influence of the sun and volcano (including the phasing), it separated out a period 1976–1996 that captured two full solar cycles, (number 21 and 22), where two explosive volcanos erupted (1991 and 1982) during active periods of strong solar cycles.

The possible mechanism could be initiated via a preferential alignment of NAO phase, generated by explosive volcanos. During that particular period, it identified certain deviations on various climate features, those include temperature around Niño 3.4 region (warming), North Atlantic region (cooling), AL (warming) and Eurasian snow cover (warming). The robustness of detected signal is established by analyzing different observational and reanalyses datasets. Consistent with temperature, a dominance of atmospheric water vapor content is also noticed. Interestingly, CMIP5 model ensemble (and also arbitrarily chosen individual models) fails to comply with such findings. It is also true for other models. This study indicates that water vapor being the most important GHG has major contributions for an observed abrupt rise in global temperature during that period. Overall the analysis suggests a change in CP ENSO and associated water vapor feedback plays a very important role in regulating global temperature behavior since 1976 that also includes ‘Hiatus’ period. It identified the signal of natural origin is different to that from CO2 led anthropogenic linear influence. Interestingly, models suggest a failure to detect such signals, which provides explanations for the long-standing puzzle of global warming hiatus.

My Summary: Warmists are picking on the wrong molecule. CO2 is plant food, H2O makes the climate.

Background from Previous post: On Solar and Climate Variability

The last solar eclipse was in 2017. The totality in the picture lasted a little more than 2 minutes, while the process lasted about 2.5 hours.

One of the great disputes in climate research is between those (IPCC) who dismiss solar cycles as a factor in climate change and those who see correlations in the past and keep seeking to understand the mechanisms. To be clear, there is considerable agreement that earth’s atmosphere can and does reduce or increase the amount of incoming solar energy (albedo effect), thereby contributing to surface warming or cooling. The science and research into the “global dimming and brightening” is discussed in the post Nature’s Sunscreen.

The above image of the eclipse is intended to remind us that humans down through history have been terrified of the sun going dark because they knew intuitively that no sun means no life. A more modern and sophisticated concern is that even slightly falling energy from the sun brings cooling, ice and death. Quite apart from the sunscreen, this post is focused a different matter, namely that changes in the sun’s output radiation cause changes in earth climate parameters. One theory of such a mechanism is espoused by Henrik Svensmark and concerns solar particles effect upon albedo. That line of research is discussed in the post The Cosmoclimatology theory

A different investigation has been advanced by Dr.Indrani Roy, her most recent publication this month being a book Climate Variability and Sunspot Activity Analysis of the Solar Influence on Climate (H/T NoTricksZone).

The book is behind a paywall, but the abstract and chapter headings indicate a comprehensive approach.

Overview Climate Variability and Sunspot Activity (2018)

This book promotes a better understanding of the role of the sun on natural climate variability. It is a comprehensive reference book that appeals to an academic audience at the graduate, post-graduate and PhD level and can be used for lectures in climatology, environmental studies and geography.

This work is the collection of lecture notes as well as synthesized analyses of published papers on the described subjects. It comprises 18 chapters and is divided into three parts: Part I discusses general circulation, climate variability, stratosphere-troposphere coupling and various teleconnections. Part II mainly explores the area of different solar influences on climate. It also discusses various oceanic features and describes ocean-atmosphere coupling. But, without prior knowledge of other important influences on the earth’s climate, the understanding of the actual role of the sun remains incomplete. Hence, Part III covers burning issues such as greenhouse gas warming, volcanic influences, ozone depletion in the stratosphere, Arctic and Antarctic sea ice, etc. At the end of the book, there are few questions and exercises for students. This book is based on the lecture series that was delivered at the University of Oulu, Finland as part of M.Sc./ PhD module.

Chapter Titles

Climatology and General Circulation
Major Modes of Variability
Stratosphere-Troposphere Coupling
Teleconnection Among Various Modes
Solar Influence Around Various Places: Robust Solar Signal on Climate
Total Solar Irradiance (TSI): Measurements and Reconstructions
Atmosphere-Ocean Coupling and Solar Variability
Ocean Coupling
The Sun and ENSO Connection–Contradictions and Reconciliations
A Debate: The Sun and the QBO
Solar Influence: ‘Top Down’ vs. ‘Bottom Up’
An Overview of Solar Influence on Climate
Other Major Influences on Climate
Sun: Atmosphere-Ocean Coupling – Possible Limitations
The Arctic and Antarctic Sea Ice
CMIP5 Project and Some Results
Green House Gas Warming
Volcanic Influences
Ozone Depletion in the Stratosphere
Influence of Various Other Solar Outputs

To better appreciate Roy’s viewpoint, two of her previous publications provide the evidence and analytical thought behind her conclusions. Published in 2010 with J.D. Haigh was Solar cycle signals in sea level pressure and sea surface temperature Excerpts in italics with my bolds.

Summary of SLP and SST signals

We identify solar cycle signals in the North Pacific in 155 years of sea level pressure and sea surface temperature data. In SLP we find in the North Pacific a weakening of the Aleutian Low and a northward shift of the Hawaiian High in response to higher solar activity, confirming the results of previous authors using different techniques. We also find a broad reduction in pressure across the equatorial region but not the negative anomaly in the sub-tropics detected by vL07. In SST we identify the warmer and cooler regions in the North Pacific found by vL07 but instead of the strong Cold Event-like signal in tropical SSTs we detect a weak WE-like pattern in the 155 year dataset.

We find that the peak SSN years of the solar cycles have often coincided with the negative phase of ENSO so that analyses, such as that of vL07, based on composites of peak SSN years find a La Nina response. As the date of peak annual SSN generally falls a year or more in advance of the broader maximum of the 11-year solar cycle it follows that the peak of the DSO is likely to be associated with an El Nino-like pattern, as seen by White et al. (1997). An El Nino pattern is clearly portrayed in our regression analysis using only data from second half of the last century, but inclusion of ENSO as an independent regression index results in a significant diminution of the solar signal in tropical SST, showing further how an ENSO signal might be interpreted as due to the Sun.

Any mechanisms proposed to explain a solar influence should be consistent with the full length of the dataset, unless there are reasons to think otherwise, and analyses which incorporate data from all years, rather than selecting only those of peak SSN, represent more coherently the difference between periods of high and low solar activity on these timescales.

The SLP signal in mid-latitudes varies in phase with solar activity, and does not show the same modulation by ENSO phase as tropical SST, suggesting that the solar influence here is not driven by coupled-atmosphere-ocean effects but possibly by the impact of changes in the stratosphere resulting in expansion of the Hadley cell and poleward shift of the subtropical jets (Haigh et al., 2005). Given that climate model results in terms of tropical Pacific SST can be dependent on different ENSO variability within the models, our analysis indicates that the robustness of any proposed mechanism of the response to variations in solar irradiance needs to be analyzed in the context of ENSO variability where timing plays a crucial role.

Comment on Dr. Roy’s Methodology

It is challenging to grasp this approach and results because she respects the complexity of solar and climate dynamics. For starters, she is not mining climate data in search of 11 year periodicities as others have done. Dr. Roy takes the dates of observed SSN maxima and minima and compares with repeated effects in climate measurements. Many readers will know that solar cycles are only quasi-11 years long; there is considerable irregularity.

Even more importantly, SSN do not peak midway in the cycle, but can appear early on and show additional peak(s) afterward. She defines minima and maxima in terms of SSN significantly lower or higher than the mean. So Roy’s analysis is not simplistic, but correlates all years in the datasets comparing SSN with climate measures.

Dr. Roy also diligently analyzes confounding factors such as oceanic circulations and the influence of previous years upon succeeding years (system momentum). For example, the above study discussed solar influence on Pacific SST and SLP. This is presented in the following image:

Tropical Pacific SST composites using NOAA Extended V4 (ERSST) data for solar Max (Top) and Min years (Bottom) during DJF. Levels usually significant up to 95% level are overlaid by opposite coloured contour. Plots are generated using IDL software, version 8 with the data from NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at (http://www.esrl.noaa.gov/psd/).

Importantly, the analysis shows little to no solar influence upon the ENSO 3.4 ocean sector, but as the graph above shows the effect is much broader. Roy concludes that ENSO operates mostly independently of solar influence. Even more striking is the result for NH winter, showing solar minima associated with generally warmer SST and maxima generally cooler. Dr. Roy explains the solar influence in terms of two separate processes. Bottom up is fluctuations in SSTs while top-down is UV effects upon the stratosphere extending downward expressed in SLP differentials.

For a discussion of the solar/climate mechanism there is Solar cyclic variability can modulate winter Arctic climate by Indrani Roy Scientific Reportsvolume 8, Article number: 4864 (2018). Excerpts in italics with my bolds.

Abstract

This study investigates the role of the eleven-year solar cycle on the Arctic climate during 1979–2016. It reveals that during those years, when the winter solar sunspot number (SSN) falls below 1.35 standard deviations (or mean value), the Arctic warming extends from the lower troposphere to high up in the upper stratosphere and vice versa when SSN is above. The warming in the atmospheric column reflects an easterly zonal wind anomaly consistent with warm air and positive geopotential height anomalies for years with minimum SSN and vice versa for the maximum. Despite the inherent limitations of statistical techniques, three different methods – Compositing, Multiple Linear Regression and Correlation – all point to a similar modulating influence of the sun on winter Arctic climate via the pathway of Arctic Oscillation. Presenting schematics, it discusses the mechanisms of how solar cycle variability influences the Arctic climate involving the stratospheric route. Compositing also detects an opposite solar signature on Eurasian snow-cover, which is a cooling during Minimum years, while warming in maximum. It is hypothesized that the reduction of ice in the Arctic and a growth in Eurasia, in recent winters, may in part, be a result of the current weaker solar cycle.

Results

In summary, for solar Min years, the warm air column is associated with positive geopotential height anomalies and an easterly wind, which reverses during Max years. Such NAM feature is clearly evident supporting the hypothesis of communicating a solar signal to Arctic via winter NAM (North Annular Mode).

Above: Mechanism to describe the stratospheric pathway for solar cycle variability to influence the Arctic climate. Mechanisms for (a) discuss a route where perturbation in the upper stratospheric polar vortex is transported downwards and impacts the Arctic on a seasonal scale via the winter NAM (flowchart is presented on the right). Mechanisms for (b) discusses the route that involves upper stratospheric polar vortex, tropical lower stratosphere, Brewer-Dobson circulation and Ferrel cell (flowchart is presented to the left). It is created using images or clip art available from Powerpoint.

During DJF, Arctic sea ice extent suggests a strong correlation with SSN (99% significant) and even with AOD (95% significant) (Table 3a). SSN is also found to be strongly correlated with AO (95% significant). Figure 8a shows that significant correlation between Arctic sea ice extent and SSN is still present in other seasons as well. However, the correlation between SSN and AO is only significant in DJF, confirming that the possible route of solar influence on winter Arctic sea ice is via the AO. On the other hand, the influence of AO on Arctic sea ice extent is not present during winter. It is strongest during JJA, though fails to exceed a significant threshold of 95% level.

Results of Correlation Coefficient (c.c) between Sea Ice Extent and various other parameters. (a) Seasonal c.c. for four different seasons are presented using other parameters as SSN and AO, and (b) c.c. for the winter season in different regions using other parameters as AO and AMO. Significant levels of 95% and 99% using a students ‘t-test’ are marked by dashed line and dotted line respectively. Plots are prepared using IDL software, version 8.

In terms of oceanic longer-term variability, here we particularly focus on the AMO and find a strong connection between sea ice and AMO in winter, agreeing with previous studies45,46. Earlier discussions suggested that there are few differences in region A and B relating to trend (Figs S6 and S7), but correlation technique indicated a very strong anti-correlation between the winter AMO index and sea ice in all regions of our considerations (Fig. 8b)). Even using two different data sources (HadSST and ERSST) we arrive at similar results, and it is also true for overall sea ice extent. It could also be possible that, in region B, due to a strong presence of AO influence of the sun, it may mask some of the influence of the longer-term trend (seen in Fig. 2) to suggest a lesser trend, as also noted in Figs S6 and S7.

This Matters As We Reach Solar Minimum for Cycle 24

The latest observations show this solar cycle is over, perhaps the next one beginning. With no sunspots seen since June, this is unusually quiet.

The solar surface at the moment is “Spotless” and has been for a month.

Summary

The sun is the primary source of energy in the earth/atmosphere system, but the actual role of the sun and related mechanisms to support varied regional climate responses and its seasonality around the world, are still poorly understood. Solar energy output varies in cycles, of which the 11-year cyclic variability is one of the most crucial ones. It causes differences in the amount of solar energy absorbed in the UV part of the spectrum within the upper stratosphere, varying from 6 to 8%. Such variation is believed to be one of the most important solar energy outputs to influence the climate of the earth and that knowledge of cyclic behaviour can also be used for future prediction purposes. Apart from solar UV related effects on earth’s climate, studies also identified effects related to solar particle precipitation.

Various studies have also detected an influence of the El Nino Southern Oscillation (ENSO)22 and the Pacific Decadal Oscillation (PDO) on Arctic sea ice. An association between the sun and ENSO are discussed in various research. Because of related complexities along with various linear and nonlinear couplings among major modes of variability, the role of the sun on Arctic air temperatures and sea ice extent and related mechanisms remains poorly understood/explored.

While many studies point to anthropogenic influences on the long-term sea ice decline, this study is motivated by the potential links between the sun and the surface climate through stratospheric processes. Alongside warming in the Arctic, a cooling is noticed around Eurasian sector despite continuing rise of greenhouse gas concentrations. Various modelling groups, however, made unsuccessful efforts to detect an association between Eurasian cooling and Arctic sea-ice decline. In this work, we evaluate the impact of the solar 11-year cycle, measured in terms of solar sunspot number (SSN), as a driving factor to modulate Arctic and surrounding climate. The influences of SSN on various surface parameters, such as Sea Level Pressure (SLP), Sea Surface Temperature (SST), and the polar stratosphere are well recognised. If there is indeed a link between the solar cycle and Arctic climate, it is possible that the 11-year solar cycle can be used to improve seasonal and decadal predictions of sea ice. In the present study, we use a combination of observational and reanalysis datasets to uncover relationships between the sun’s variability and Arctic surface climate, via the modulation of NAM and downward propagation of anomaly from upper stratospheric winter polar vortex.

Our result suggests the latest rapid decline of sea ice around the Arctic in the recent winter decade/season could also have contributions from the current weaker solar cycle. The last 14 years are dominated by solar Min years and have only one Max. This is unlike other previous years, where the number of Max and Min years were evenly distributed (five each). The cumulative effect from the past 13 solar Min years could have played a role in the current record decline of the last winter, 2017. The current weaker solar cycle may also have contributions on increase in winter snow cover around the Eurasian sector.

Presenting schematics and flowcharts, we discussed mechanisms of how solar cycle variability influences Arctic climate. In the first route, perturbation in the upper stratospheric polar vortex is transported downwards and modulates the Arctic in a seasonal scale via the winter NAM. Another route was shown, which could involve upper stratospheric polar vortex, tropical lower stratosphere, Brewer-Dobson circulation and Ferrel cell. It could also reinforce the findings of the ‘Solar Max (Min) – cold (warm) Arctic’ scenario.

N. Atlantic Still Cooling in 2018

Posted on October 10, 2018 by Ron Clutz

RAPID Array measuring North Atlantic SSTs.

For the last few years, observers have been speculating about when the North Atlantic will start the next phase shift from warm to cold.

Source: Energy and Education Canada

An example is this report in May 2015 The Atlantic is entering a cool phase that will change the world’s weather by Gerald McCarthy and Evan Haigh of the RAPID Atlantic monitoring project. Excerpts in italics with my bolds.

This is known as the Atlantic Multidecadal Oscillation (AMO), and the transition between its positive and negative phases can be very rapid. For example, Atlantic temperatures declined by 0.1ºC per decade from the 1940s to the 1970s. By comparison, global surface warming is estimated at 0.5ºC per century – a rate twice as slow.

In many parts of the world, the AMO has been linked with decade-long temperature and rainfall trends. Certainly – and perhaps obviously – the mean temperature of islands downwind of the Atlantic such as Britain and Ireland show almost exactly the same temperature fluctuations as the AMO.

Atlantic oscillations are associated with the frequency of hurricanes and droughts. When the AMO is in the warm phase, there are more hurricanes in the Atlantic and droughts in the US Midwest tend to be more frequent and prolonged. In the Pacific Northwest, a positive AMO leads to more rainfall.

A negative AMO (cooler ocean) is associated with reduced rainfall in the vulnerable Sahel region of Africa. The prolonged negative AMO was associated with the infamous Ethiopian famine in the mid-1980s. In the UK it tends to mean reduced summer rainfall – the mythical “barbeque summer”.Our results show that ocean circulation responds to the first mode of Atlantic atmospheric forcing, the North Atlantic Oscillation, through circulation changes between the subtropical and subpolar gyres – the intergyre region. This a major influence on the wind patterns and the heat transferred between the atmosphere and ocean.

The observations that we do have of the Atlantic overturning circulation over the past ten years show that it is declining. As a result, we expect the AMO is moving to a negative (colder surface waters) phase. This is consistent with observations of temperature in the North Atlantic.

Cold “blobs” in North Atlantic have been reported, but they are usually a winter phenomena. For example in April 2016, the sst anomalies looked like this

But by September, the picture changed to this

And we know from Kaplan AMO dataset, that 2016 summer SSTs were right up there with 1998 and 2010 as the highest recorded.

As the graph above suggests, this body of water is also important for tropical cyclones, since warmer water provides more energy. But those are annual averages, and I am interested in the summer pulses of warm water into the Arctic. As I have noted in my monthly HadSST3 reports, most summers since 2003 there have been warm pulses in the north atlantic.
AMO September 2018 The AMO Index is from from Kaplan SST v2, the unaltered and untrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N. The graph shows warming began after 1992 up to 1998, with a series of matching years since. September is the second hottest month in the dataset, and note the considerable drop from 2017 to August 2018. Because McCarthy refers to hints of cooling to come in the N. Atlantic, let’s take a closer look at some AMO years in the last 2 decades.

AMO decade 092018

With all the talk of AMOC slowing down and a phase shift in the North Atlantic, we expect that the annual average for 2018 will confirm that cooling has set in. Through September the momentum is certainly heading downward, despite the band of warming ocean that gave rise to now receding European heat waves.
cdas-sflux_sst_atl_1

Climate Science, Ethics and Religion

Posted on October 6, 2018 by Ron Clutz

Thanks to an insightful post at Climate Scepticism (here), we have a recent quote from former US President Obama:

“You have to believe in facts. Without facts there’s no basis for cooperation. If I say this is a podium and you say this is an elephant, it’s going to be hard for us to cooperate…I can’t find common ground if somebody says climate change is just not happening, when almost all of the world’s scientists tell us it is.”

This statement is the starting point for that poster to explore ways that even the most accomplished scientists have in the past shared beliefs that were valid only as fashionable at the time. In this post, I want to consider first why a lawyer like Obama gets science wrong, and secondly to consider the moral and religious confusion regarding our climate.

Science is Trial And Error, not Case Law

In the legal world, cases are judged and rulings become precedent for later cases that arise. Thus principles become established, settled facts for jurists to follow. Scientists operate in a different world, one where experiments provide evidence that an assumption successfully predicts how things work in nature. But that premise can be overturned by subsequent experiments, so scientific laws are always tentative.

In short, lawyers proceed by deduction, going from the accepted generality to the particular instance. Scientists refer to generalities, but induction is their primary method of discovery. Science proceeds from the particular to arrive at general conclusions, sometimes overturning a previous generality.

A previous post Degrees of Climate Truth was based upon work by Andy May in discussing how climate assertions can be seen in various stages of development toward scientific truth.

072516_1631_factsandthe1

In Table 1 we can see that the comparison of man-made climate change and the possibility of a man-made climate catastrophe are not really comparable to the theories of gravity and evolution. Man-made climate change is more than an idea, it is based on some observations and reasonable models of the process have been developed and can be tested. But, none of the models have successfully predicted any climatic events. Thus, they are still a work-in-progress and not admissible as evidence supporting a scientific theory.

Ethical and Religious Dimensions

Climate assertions come from people based on moral and religious frameworks.

This post is background to exploring the ethical and religious dimensions of the climate change movement. It is also important to recognize the human journey regarding morality.

Moral Models

The ethic of Good vs. Evil is a teleological paradigm, going all the way back to Plato, but still a reference for some today. This model asserts that values can be determined as eternal truths, applicable in all times and places.

Most people have moved to an ethic of Right vs. Wrong, a legal paradigm. Here morality is relative to a society that determines what is morally acceptable or not. And of course, there are variations both among different places, and within a single society over time.

Modern ethics has taken an additional step to an ethic of Responsibility vs. Irresponsibility, a contextual paradigm. Now moral behavior seeks the largest possible context: “the greatest good for the greatest number.” This can lead to some strange choices, such as suicide bombers or pro-life advocates who justify murdering abortion clinic doctors. The perversion arises when an actor excludes some living things, or whole classes of creatures from the context of responsibility.

Summary: Climate Morality

Some climate activists/alarmists are operating with a good vs. evil model, in which their understanding of good separates people into sheep and goats. Describing others as “deniers” shows this clearly. And in the recent US senate supreme court nomination hearings we have an additional stark reminder that members of even advanced societies can seek to disqualify others as human beings, not simply block them from positions of responsibility.

Obama is clearly operating in the right vs. wrong model, as expected given his legalistic education. Since laws and legal principles are relative to a social framework and heritage, social proof is all that is required for him to accept climate assertions as true. At the same time, that mentality requires dismissing and demeaning the viewpoints contrary to the consensus. Such tribalism is contrary to scientific discourse, and in the extreme case like Rwanda the others can be considered “cockroaches” and exterminated.

It should be clear that when climate alarmists appeal to saving the planet for future generations, they are applying contextual ethics. Less obvious is the ancient religious notion that by making sacrifices, we humans can assure more favorable weather. These days, fossil fuels have become the sacrificial lamb required by Mother Nature to play nice with human beings. In the past, people made images and worshiped them, thinking that they could control nature in that way. These days, we make computer models whose projections are sure to scare the bejesus out of us.

See also: What’s wrong with the legal brief on climate change Facts Omitted by Climatists

mrz092215dapr_s878x638

Cooling by Land, or Cooling by Sea?

Posted on October 4, 2018 by Ron Clutz

With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea. UAH has updated their tlt (temperatures in lower troposphere) dataset for September. Previously I have done posts on their reading of ocean air temps as a prelude to updated records from HADSST3. This month I will add a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years.

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. 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?

The August update to HadSST3 will appear later this month, but in the meantime we can look at lower troposphere temperatures (TLT) from UAHv6 which are already posted for August. The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above.

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 temps since January 2015.

UAH Oceans 201809 The anomalies over the entire ocean dropped to the same value, 0.12C in August (Tropics were 0.13C). Warming in previous months was erased, and September added very little warming back.

Taking a longer view, we can look at the record since 1995, that year being an ENSO neutral year and thus a reasonable starting point for considering the past two decades. On that basis we can see the plateau in ocean temps is persisting. Since last October all oceans have cooled, with offsetting bumps up and down.

UAHv6 TLT Monthly Ocean Anomalies	Average Since 1995	Ocean 9/2018
Global	0.13	0.15
NH	0.16	0.18
SH	0.11	0.13
Tropics	0.12	0.22

As of September 2018, Global ocean air temps as well as SH and SH are nearly the average since 1995. The Tropics bumped upward last month. Globally, in NH and the Tropics, 2018 is the coolest September since 2014. The SH ocean air temps are the coolest September since 2013

Land Air Temperatures Plunged in September.

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 record air temps at 2 meters above ground. UAH gives tlt anomalies for air over land separately from ocean air temps. The graph updated for September is below.
UAH Land 201809

The greater volatility of the Land temperatures is evident, and also the dominance of NH, which has twice as much land area as SH. Note how global peaks mirror NH peaks. Thus the importance of the recent drops in NH and SH driving global land temps downward. A table for Land temperatures is below, comparable to the one for Oceans.

UAHv6 TLT Monthly Land Anomalies	Average Since 1995	Land 9/2018
Global	0.21	0.13
NH	0.23	0.10
SH	0.12	0.14
Tropics	0.14	0.24

In the longer term since 1995, Globally and in NH land temps are well below the average anomalies, while SH is nearly average, and the Tropics above average (though comprising limited surface area).

Summary

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps. It is striking to now see NH and Global land temps dropping rapidly. TLT measures started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern. It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995. Of course, the future has not yet been written.

On the Motion of the Ocean

Posted on September 29, 2018 by Ron Clutz

The image shows what is known about how ocean currents flow under the influence of the earth’s rotation. A recent article adds another level of complexity and insight by examining smaller scale effects. From the American Institute of Physics Researchers challenge our assumptions on the effects of planetary rotation Excerpts in italics with my bolds.

Coriolis effect can influence eddies in wakes as small as 10 meters

The Coriolis effect impacts global patterns and currents, and its magnitude, relative to the magnitude of inertial forces, is expressed by the Rossby number. For over 100 years, scientists have believed that the higher this number, the less likely Coriolis effect influences oceanic or atmospheric events. Recently, however, researchers found that smaller ocean disturbances with high Rossby numbers are influenced by the Coriolis effect. Their discovery challenges assumptions of theoretical oceanography and geophysical fluid dynamics.

A 2-D image of the velocity in an internal jet with the Rossby number of 100 that shows how planetary rotation leads to the destabilization and dispersion of an initially coherent flow pattern. Credit: Timour Radko and David Lorfeld

Earth’s rotation causes the Coriolis effect, which deflects massive air and water flows toward the right in the Northern Hemisphere and toward the left in the Southern Hemisphere. This phenomenon greatly impacts global wind patterns and ocean currents, and is only significant for large-scale and long-duration geophysical phenomena such as hurricanes. The magnitude of the Coriolis effect, relative to the magnitude of inertial forces, is expressed by the Rossby number. For over 100 years, scientists have believed that the higher this number, the less likely Coriolis effect influences oceanic or atmospheric events.

Recently, researchers at the Naval Postgraduate School in California found that even smaller ocean disturbances with high Rossby numbers, like vortices within submarine wakes, are influenced by the Coriolis effect. Their discovery challenges assumptions at the very foundation of theoretical oceanography and geophysical fluid dynamics. The team reports their findings in Physics of Fluids, from AIP Publishing.

“We have discovered some major — and largely overlooked — phenomena in fundamental fluid dynamics that pertain to the way the Earth’s rotation influences various geophysical flows,” Timour Radko, an oceanography professor and author on the paper, said.

Radko and Lt. Cmdr. David Lorfeld originally focused on developing novel submarine detection systems. They approached this issue by investigating pancake vortices, or flattened, elongated mini-eddies located in the wakes of submerged vehicles. Eddies are caused by swirling water and a reverse current from waterflow turbulence.

Last year, a team led by Radko published a paper in the same AIP journal on the rotational control of pancake vortices, the first paper that challenged the famous “Rossby rule.” In this most recent paper, the researchers showed, through numerical simulations, that internal jets of the wake can be directly controlled by rotation. They also demonstrated that the evolution of a disorganized fine-scale eddy field is determined by planetary rotation.

“Here is where our discovery could be critical,” Radko said. “We find that cyclones persist, but that anticyclones unravel relatively quickly. If the anticyclones in the wake are as strong as the cyclones, this means that the wake is fresh — the enemy passed through not too long ago. If the cyclones are much stronger than the anticyclones, then the sub is probably long gone.”

The algorithm that the researchers developed is based on the dissimilar evolution of cyclones and anticyclones, which is a consequence of planetary rotation. “Therefore,” Radko concluded, “such effects must be considered in the numerical and theoretical models of finescale oceanic processes in the range of 10-100 meters.”

The computer model is detailed enough to resolve eddies that are important for ocean circulation. The triangle-shaped island of Newfoundland, center, is at the eastern edge of the study area, the mouth of the Gulf of St. Lawrence. This graphic shows oxygen at the surface, where red shows more oxygen. Credit: Mariona Claret/University of Washington

Background: Ocean Physics in a Cup of Coffee

Tweak the Sun’s Rotation, and We’re Not Here

Posted on September 24, 2018 by Ron Clutz

Watch the Sun rotate for over a month brought to you by SDO. Since the Sun rotates once every 27 days on average, this movie presents more than an entire solar rotation. From March 30 through Apr. 29, 2011, the Sun sported quite a few active regions and magnetic loops. The movie shows the Sun in the 171 Angstrom wavelength of extreme ultraviolet light (capturing ionized iron heated to about 600,000 degrees), color coded to appear gold. The movie is based on a frame taken every 15 minutes being shown at 24 frames per second, with very few data gaps in this almost two-minute movie. Source Solar Dynamics Observatory

Another fresh reminder we owe our existence to the sun along with the climate in which we evolved and adapted. The Forbes article is Early Sun’s ‘Goldilocks’ Rotation Rate May Be Why We’re Here Excerpts in italics with my bolds.

Our early Sun’s rate of rotation may be one reason we’re here to talk about it, astrobiologists now say. The key likely lies in the fact that between the first hundred million to the first billion years of its life, our G-dwarf star likely had a ‘Goldilocks’ rotation rate; neither too slow nor too fast.

Instead, its hypothetical ‘intermediate’ few days rate of rotation guaranteed our Sun was active enough to rid our newly-formed Earth of its inhospitable, hydrogen-rich primary atmosphere. This would have enabled a more habitable, secondary atmosphere composed of nitrogen, carbon dioxide, hydrogen and oxygen to eventually form.

If it had been a ‘fast’ (less than one day rotator), our Sun might have continually stripped our young planet of its secondary atmosphere as well. However, if it took more than 10 days to rotate, it might not have been active enough to strip Earth of its hypothetical primary atmosphere.

Such ideas were recently bandied about in oral presentations at last month’s the General Assembly of the International Astronomical Union (IAU) in Vienna.

Earth’s very first atmosphere would have been too hot and too thick, more like Venus’ present-day atmosphere, Theresa Luftinger, an astrophysicist at the University of Vienna, told me. No known organisms could have evolved under such an atmosphere. A secondary atmosphere cannot evolve in the presence of a primordial atmosphere , says Luftinger.

It’s the star’s magnetic dynamo that drives its magnetic fields. And these magnetic fields, in turn, interact with the star itself, creating an interplay of extreme stellar activity.

“So, the quicker the star rotates, the higher the interaction between the magnetic field and the stellar body ,” said Luftinger.

Faster rotation means higher extreme ultra-violet and x-ray activity, Helmut Lammer, an astrophysicist at Austria’s Space Science Institute in Graz, told me. This would lead to atmospheric stripping and water loss on earthlike planets around an active young star, he says.

Our Sun is now a very slow rotator at 27 days. But that wasn’t always the case. As for why some stars seem to inherently rotate faster than others?

Astrophysicists suspect that initial conditions within star-forming clouds cause newborn stars to have different rotation rates.

Researchers are able to roughly pinpoint the Sun’s early rotation rates by studying the isotopic ratios of neon, argon, potassium, and uranium here in Earth’s crust. That is, elements which have atoms that have the same numbers of protons in their atomic nucleus, but different numbers of neutrons. The researchers also considered such isotopic ratios from decades’-old Venus surface samples taken by Soviet Venus lander missions.

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