Guest Post by Bob Tisdale
UPDATE: See the update below Figure 1.24-8 for the IPCC’s correction of their Figure 9.17 from the 5th Assessment Report. Thanks, Nic.
# # #
This post presents model-data comparisons of ocean heat content. It is a correction and update (with 2015 values) of the modeled and observed trends in ocean heat accumulation that were presented in Chapter 1.24 – A Rough Calculation of the Amount of Missing Heat…A Critical Issue of my free ebook On Global Warming and the Illusion of Control – Part 1 (25MB). In that chapter of the book, many of the model trends listed on the graphs were in error, with the trend values too low, bringing them closer to the observations. In other words, my errors favored the models. My apologies for the mistakes. Those errors have been corrected in this post and will be corrected in the ebook in its final release later this year.
# # #
Chapter 1.24 – A Rough Calculation of the Amount of Missing Heat…A Critical Issue
If you’re new to the topic of global warming, you may think discussions of The Missing Heat have to do with the slowdown in global surface warming since the late 1990s, which wasn’t anticipated by climate modeling groups. The term Missing Heat, however, is not related to the slowdown in global surface warming. Missing Heat is used in discussions of the heat being stored in the depths of the oceans.
In Chapter 1.10 – Introduction to Radiative Imbalance, I presented the modeled energy imbalance at the top of the atmosphere (TOA). As you’ll recall, there was a very wide spread in the individual model simulations of the top-of-the-atmosphere energy imbalance. (See Figure 1.10-3.) I’ve shortened the timeframe to 1955-2015 in Figure 1.24-1, which is the period for which ocean heat content data are available from the NODC.
NOTE: Following an introduction to the concept of Earth’s energy imbalance, much of Chapter 1.10 – Introduction to Radiative Imbalance is based on the August 2015 post No Consensus: Earth’s Top of Atmosphere Energy Imbalance in CMIP5-Archived (IPCC AR5) Climate Models. [End note.]
Ponder that graph for a moment. The average top-of-the-atmosphere energy imbalance (red curve) in recent years is in the expected range…the range we’ve been told by the climate science community. Example: According to Trenberth et al. (2014) Earth’s Energy Imbalance:
All estimates (OHC and TOA) show that over the past decade the energy imbalance ranges between about 0.5 and 1 Wm-2.
Trenberth et al. (2014) must not have been referring to the individual climate models, because they show a much larger range. In fact, some of the models show relatively high positive TOA energy imbalances, in the neighborhood of +2.5 watts/m^2, while others show negative energy imbalances, roughly -2.5 watts/m^2.
The simulated oceans in the models with the high positive top-of-the-atmosphere energy imbalances have to be accumulating heat at relatively fast rates. On the other hand, the simulated oceans in the models with the negative top-of-the-atmosphere energy imbalances have to be losing heat very quickly. Yes, losing heat.
In this chapter, we’re going to calculate and illustrate the ocean heat accumulation from 1955 to 2015 based on the climate-model-simulated top-of-the-atmosphere energy imbalances for all of the models included in the earlier energy imbalance chapter (Chapter 1.10). We’ll start with the full oceans compared to data for the top 2000 meters, and we’ll then compare models and data for the top 700 meters.
Because the oceans to depth have a tremendous capacity to store heat, they are supposed to be storing about 93% of the excess heat created by the emissions of man-made greenhouse gases. See Figure 1.24-2.
The pie chart in Figure 1.24-2 is based on the Earth’s total energy change inventory from Box 3.1 of Chapter 3 – Observations: Oceans of the IPCC’s 5th Assessment Report. There they write:
Ocean warming dominates the total energy change inventory, accounting for roughly 93% on average from 1971 to 2010 (high confidence). The upper ocean (0-700 m) accounts for about 64% of the total energy change inventory. Melting ice (including Arctic sea ice, ice sheets and glaciers) accounts for 3% of the total, and warming of the continents 3%. Warming of the atmosphere makes up the remaining 1%.
Later in the chapter we’ll address the depths of 0-700 meters.
WE CAN USE THE MODELED ENERGY IMBALANCE AT THE TOP OF THE ATMOSPHERE TO DETERMINE HOW MUCH HEAT THE OCEANS SHOULD BE ACCUMULATING, ACCORDING TO THE MODELS
The ocean heat content outputs of the climate models stored in the Climate Model Intercomparison Project Phase 5 (CMIP5) archive are not available in easy-to-use form at the KNMI Climate Explorer. In fact, I know of no place where ocean heat content outputs are easy to access for any of the CMIP5-based models.
Fortunately, the components of the modeled Energy Imbalance at the Top of the Atmosphere (TOA) are available. So we can determine the energy imbalance, and, in turn, how much heat the oceans should be storing, according to the models. It’s commonly done, as you’ll see.
As you’ll recall from Chapter 1.10 – Introduction to Radiative Imbalance, the energy imbalance at the top of the atmosphere is made up of 3 components (nomenclature and acronym used at the KNMI Climate Explorer are shown in parentheses):
- the amount of sunlight reaching the top of the atmosphere (TOA Incident Shortwave Radiation, rsdt),
- the sunlight being reflected back to space primarily by clouds and volcanic aerosols (TOA Outgoing Shortwave Radiation, rsut), and
- the infrared radiation being emitted by Earth relative to the top of the atmosphere (TOA Outgoing Longwave Radiation, rlut).
The top of the atmosphere energy imbalance is calculated by subtracting the Outgoing Shortwave and Longwave Radiation from Incident Shortwave Radiation.
Figure 1.24-3 presents the average top-of-the-atmosphere energy imbalance of the climate models stored in the CMIP5 archive, specifically the multi-model mean of the models using historic and RCP6.0 forcings. We’re discussing the multi-model mean now for simplicity sake…for those new to the topic. The 1955 to 2015 timeframe relates to the NODC’s ocean heat content data for the depths 0-2000 meters (about 6600 feet or about 1.25 miles).
The large dips and rebounds in those model simulations are caused by the aerosols emitted into the stratosphere by explosive volcanic eruptions.
Each year that the top-of-the-atmosphere energy imbalance is positive, the oceans gain heat, and each year the top-of-the-atmosphere energy imbalance is negative, the oceans lose heat. The energy imbalance is positive most of the time, so the modeled oceans should be warming to depth, according to the model mean.
Note: You’ll notice in the title block of Figure 1.24-3 that I excluded three models: one CESM-CAM5 model and two IPSL models. There were shifts at 2006 in the TOA Outgoing Longwave Radiation outputs of all three runs of the CESM-CAM5 model (one with a monstrous shift), which skewed the multi-model mean of that metric for that scenario. (I notified KNMI of that problem, and NCAR has since corrected them. I’ve continued to exclude them so that the models in this chapter are the same in the top-of-the-atmosphere energy imbalance chapter.) I also excluded the two IPSL models because their top-of-the-atmosphere Incident Shortwave Radiation contains a volcanic aerosol component, while all other models do not. (The other models address volcanic aerosols with the Outgoing Shortwave Radiation.)
That leaves 21 models, including BCC-CSM1-1, BCC-CSM1-1-M, CCSM4 (6 runs), CSIRO-MK3-6-0 (10 runs), FIO-ESM (3 runs), GFDL-CM3, GFDL-ESM2G, GISS-E2-H p1, GISS-E2-H p2, GISS-E2-H p3, GISS-E2-R p1, GISS-E2-R p2, GISS-E2-R p3, HadGEM2-AO, HadGEM2-ES (3 runs), MIROC5 (3 runs), MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, NorESM1-M, and NorESM1-ME.
For those models with multiple runs, the ensemble members are averaged before being included in the multi-model mean.
CONVERTING FROM WATTS/M^2 TO JOULES*10^22/YEAR
We’ll need to convert the units of the modeled top-of-the-atmosphere energy imbalance (watts/m^2) to those used for ocean heat content to the depths of 2000 meters (Joules * 10^22).
Apparently, the climate science community also uses TOA energy imbalance to determine ocean heat uptake. Gavin Schmidt presented two conversion factors (the one he originally used in his model-data comparisons at RealClimate and the corrected one) in his post OHC Model/Obs Comparison Errata. Dr. Schmidt writes:
My error was in assuming that the model output (which were in units W yr/m2) were scaled for the ocean area only, when in fact they were scaled for the entire global surface area (see fig. 2 in Hansen et al, 2005). Therefore, in converting to units of 1022 Joules for the absolute ocean heat content change, I had used a factor of 1.1 (0.7 x 5.1 x 365 x 3600 x 24 x 10-8), instead of the correct value of 1.61 (5.1 x 365 x 3600 x 24 x 10-8).
The paper Dr. Schmidt referenced is Hansen et al. (2005) Earth’s energy imbalance: Confirmation and implications. That papers notes:
Ocean heat storage. Confirmation of the planetary energy imbalance can be obtained by measuring the heat content of the ocean, which must be the principal reservoir for excess energy (3, 15). Levitus et al. (15) compiled ocean temperature data that yielded increased ocean heat content of about 10 W year/m2, averaged over the Earth’s surface, during 1955 to 1998 [1 W year/m2 over the full Earth ~ 1.61 x 1022 J…].
Referring back to Dr. Schmidt’s post at RealClimate, unfortunately, Gavin didn’t present the units for those factors. So let’s add the units:
1.61*10^22 Joules/year per watt/m^2 = (Earth’s surface area 5.1*10^14 m^2)*(365 days/year)*(3600 seconds/hour)*(24 hours/day)
We’ll make one more adjustment to the conversion factor. We’ll assume the oceans are accumulating 93% of the top-of-the-atmosphere energy imbalance, which lowers the conversion factor to 1.50*10^22 Joules/year per watt/m^2.
MODELED ANNUAL OCEAN HEAT UPTAKE AND ACCUMULATION BASED ON THE MODEL MEAN (FULL OCEAN)
Based on that conversion factor, the annual modeled ocean heat uptake (absolute) for the full oceans that was derived from the simulated top-of-the-atmosphere energy imbalance are shown in Figure 1.24-4…again using the model mean to simplify these early discussions. Basically, Figure 1.24-4 illustrates the average of the modeled top-of-the-atmosphere energy imbalance but in terms used for ocean heat content. Every year the value is positive, the oceans gain heat, and each year the value is negative, the oceans lose heat. The difference between an annual value and zero indicates how much heat the oceans gain or lose in a given year. In other words, the graph shows the annual ocean warming and cooling rates for the global oceans.
But that still doesn’t allow us to directly compare the models to the data. The (much-adjusted) global ocean heat content data from the NODC for the depths of 0-2000 meters are presenting how much heat the oceans are accumulating in the top 2000 meters. To determine the modeled ocean heat accumulation, we simply take a running total (cumulative sum) of the annual heat uptake…like the balance in a bank account. See Figure 1.24-5.
I’ve included the NODC ocean heat content reconstruction for the top 2000 meters (zeroed at 1957) in pentadal form as a reference for the (much-adjusted) observations. (Data here.) The data have been shifted so that the 1957 value is zero. That was done solely to ease the visual comparisons. Keep in mind, before the early 2000s when the ARGO floats were deployed, the NODC ocean heat content data for the top 2000 meters are based on very few temperature and salinity measurements. Phrased differently, before the ARGO era, the NODC ocean heat content data to depths of 2000 meters are basically make-believe data. We’ll discuss and illustrate this in more detail in the future Part 2 of this book.
For the sake of discussion, we’ll assume there is no heat gain below 2000 meters. It’s commonly done. That is, we’ll assume all of the excess heat is being absorbed only in the top 2000 meters. That’s consistent with the findings of Liang et al. (2015) Vertical Redistribution of Oceanic Heat Content. (See the preprint copy here.) In fact, Liang et al. found (1) the oceans below 2000 meters had cooled from 1992 to 2012 and (2) part of the heat above 2000 meters was from the redistribution of heat upwards from the depths below 2000 meters. By assuming all of the observed heat gain is in the top 2000 meters, we can then compare the data to the model outputs, the latter of which are for the full ocean, from surface to floor.
With those things considered, it might be misleadingly said that the models, as represented by the model mean, do a reasonable job of simulating the observed warming rate of the oceans. Why misleadingly? As we’ve already shown (Figure 1.24-1), there is no agreement among the models on the energy imbalance at the top of the atmosphere, and that means there is no agreement among the models on how much heat the oceans are accumulating…if they are in fact accumulating and not losing heat in their modeled oceans.
MODELED ANNUAL OCEAN HEAT UPTAKE AND ACCUMULATION FOR ALL MODELS (FULL OCEAN)
Using the conversion factor presented earlier (1.50*10^22 Joules/year per watt/m^2), the annual heat uptake and losses (absolute) in modeled ocean heat content, for the full oceans, based on the simulated top-of-the-atmosphere energy imbalance from 1955 to 2015, are shown in Figure 1.24-6…this time for the model mean (in red) and the individual models stored in the CMIP5 archive using the historic and RCP6.0 forcings. Again, in other words, Figure 1.24-6 illustrates the modeled energy imbalance of the individual models but in the terms used for ocean heat content, which is why it looks so similar to Figure 1.24-1.
The simulated oceans in the models with higher absolute positive values are gaining heat faster than those whose energy imbalances are closer to zero. And the oceans in the models with the negative imbalances from 1955 to 2015 are not accumulating heat; they’re losing it.
The models with the high positive imbalances and with the negative imbalances are obvious outliers. They create remarkable ocean heat content curves that should probably be considered implausible. See Figure 1.24-7. Yet they are among the models used by the IPCC for their 5th Assessment Report. Then again, if we were to eliminate models because they didn’t simulate some metric properly, there would be no climate models left in the CMIP archives.
Obviously, based on the climate models used by the IPCC for their 5th Assessment Report, there is no agreement on how much heat the oceans should be accumulating, or even if the oceans are accumulating heat, based on their energy imbalances. And the differences in the simulated ocean heat accumulation are so great that using the model mean to represent the models is very misleading.
THE IPCC’S PRESENTATION IS TOTALLY DIFFERENT
Figure 1.24-8 is Figure 9.17 from Chapter 9 – Evaluation of Climate Models from the IPCC’s 5th Assessment Report. The middle graph, Cell b, corresponds to my Figure 1.24-7 above. Surprisingly, there are few similarities between the two presentations. The primary difference between the IPCC’s and my comparison graphs of ocean heat accumulation is that the IPCC has adjusted the climate model outputs in its graph. I’ve underlined in red the sentence where the IPCC states that in the caption. It reads:
Simulation drift has been removed from all CMIP5 runs with a contemporaneous portion of a quadratic fit to each corresponding pre-industrial control run (Gleckler et al., 2012).
UPDATE: Nic Lewis advises on the thread of the cross post at WUWT that Figure 9.17 from the IPCC’s 5th Assessment Report had been in error and that they issued a correction for it in the IPCC’s errata for that report. My Figure 1.24-8 above includes the original illustration that was in error. (Link to all corrected illustrations are here, and the corrected Figure 9.17 is here.) The corrected graphs from their Figure 9.17 are included in my Figure 1.24-8 Supplement.
Figure 1.24-8 Supplement
The corrections appear to have changed only the red “CMIP5 mean” curves, raising them so that they are now greater than the observations. Thanks, Nic.
That does not alter the fact that the IPCC’s illustration bears no resemblance to my Figure 1.24-7.
Note: Climate model “drift” is, basically, a phenomenon where the climate model outputs can change with time even if the inputs were to be kept constant. The problem is discussed in Sen Gupta et al. (2013) Climate Drift in CMIP5 Models (paywalled). The abstract reads (my boldface):
Climate models often exhibit spurious long-term changes independent of either internal variability or changes to external forcing. Such changes, referred to as model “drift,” may distort the estimate of forced change in transient climate simulations. The importance of drift is examined in comparison to historical trends over recent decades in the Coupled Model Intercomparison Project (CMIP). Comparison based on a selection of metrics suggests a significant overall reduction in the magnitude of drift from phase 3 of CMIP (CMIP3) to phase 5 of CMIP (CMIP5). The direction of both ocean and atmospheric drift is systematically biased in some models introducing statistically significant drift in globally averaged metrics. Nevertheless, for most models globally averaged drift remains weak compared to the associated forced trends and is often smaller than the difference between trends derived from different ensemble members or the error introduced by the aliasing of natural variability. An exception to this is metrics that include the deep ocean (e.g., steric sea level) where drift can dominate in forced simulations. In such circumstances drift must be corrected for using information from concurrent control experiments. Many CMIP5 models now include ocean biogeochemistry. Like physical models, biogeochemical models generally undergo long spinup integrations to minimize drift. Nevertheless, based on a limited subset of models, it is found that drift is an important consideration and must be accounted for. For properties or regions where drift is important, the drift correction method must be carefully considered. The use of a drift estimate based on the full control time series is recommended to minimize the contamination of the drift estimate by internal variability.
In other words, because of model flaws, climate model outputs are adjusted by climate scientists when simulating the deep oceans. Let’s rephrase that: Because of inherent flaws in climate models, when examining model performance, climate scientists will adjust the outputs of climate models before comparing them to data. How bizarre is that?
LET’S RETURN TO OUR TOA ENERGY IMBALANCE-BASED OCEAN HEAT CONTENT AND ELIMINATE THE OUTLIERS (FULL OCEAN)
Three of the 21 models in Figure 3.24-7 are showing way too much heat accumulation, and 5 of the models show the oceans losing heat because of their negative TOP-OF-THE-ATMOSPHERE energy imbalances. They are so far from the much-adjusted observations I’ve excluded them in Figure 1.24-9.
But eliminating the outliers creates other problems for the models. With the obvious outliers removed, not one of the remaining 13 models has an ocean heat content trend that’s lower than the observed trend. In other words, all of the models are showing too much warming. And that means, for most the remaining models, the climate sensitivities to CO2 are too high.
With the outliers removed, according to the model mean, the models are showing a heat accumulation that’s more than 2 times higher than observed.
WHAT ABOUT THE GISS MODEL E2 SIMULATIONS FROM THE CMIP5 ARCHIVE (FULL OCEAN)?
For a number of years, Gavin Schmidt (the director of the Goddard Institute of Space Studies, GISS) presented model-data comparisons at RealClimate that included simulated and observed ocean heat content for different depths. Gavin compared models and data for the depths of 0-700 meters in the posts that appeared in December 2009, May 2010, January 2011 and February 2012. It was only in the last post that Dr. Schmidt presented the comparison for the modeled full ocean and data for 0-2000 meters. We’ll illustrate the model-data comparison for the top 700 meters in a moment, but let’s first stick with the data for the top 2000 meters and the modeled ocean heat accumulation for the full oceans.
You’ll note that the ocean heat content graphs in those RealClimate posts have been corrected per Gavin’s May 2012 post OHC Model/Obs Comparison Errata. The ocean heat content comparisons in them used the GISS models from the earlier CMIP3 archive. Gavin Schmidt closed his errata post with:
Analyses of the CMIP5 models will provide some insight here since the historical simulations have been extended to 2012 (including the last solar minimum), and have updated aerosol emissions. Watch this space.
I suspect some of you, like me, have been patiently waiting for those CMIP5-archived GISS Model E2-based model-data comparisons for ocean heat content. Yet, for more than four years, none have been posted at RealClimate.
For those interested, Figure 1.24-10 compares the data with the top-of-the-atmosphere energy imbalance-based ocean heat content for the three GISS Model E2-R simulations…along with the mean of those 3 runs. The “R” suffix letter stands for the Russell Ocean model that’s coupled to the GISS Model E.
This batch of GISS climate models is showing that they are too sensitive to CO2 by a wide amount. The modeled heat accumulation shown by the model mean of this GISS model more than doubles that shown by the data.
Looking at the legends in Figures 1.24-6, -7 and -9, you’ll note that GISS also has another group of model experiments with an “H” suffix. The “H” stands for HYCOM ocean. Figure 1.24-11 includes the model-data comparison of the GISS models with the HYCOM oceans.
Once again, the GISS models show they are way too sensitive to CO2. The model mean of this GISS model shows a heat accumulation that’s more than twice the observations.
I’ll let you speculate about why there have been no model-data comparisons of ocean heat content at RealClimate for 4 years.
MODEL-DATA FOR 0-700 METERS
We’ll be using the annual NODC ocean heat content data (0-700m) here in the following comparisons. For the comparisons I’ve simply shifted the data so that the 1955 value is zero.
There is better sampling at the depths of 0-700 meters than at 700-2000 meters before the ARGO era, so the NODC ocean heat content data for the depths of 0-700 meters is a better dataset. While sampling at these upper depths may be better globally, they were still very poor in the southern hemisphere before the deployment of the ARGO floats. With that in mind…
For these comparisons we’ll rely on the IPCC’s statement that “The upper ocean (0-700 m) accounts for about 64% of the total energy change inventory…” from the earlier quote. That is, we’re taking the scaling factor (1.61*10^22 Joules/year per watt/m^2) and multiplying it by 0.64 to determine the annual ocean heat content uptake for the top 700 meters of the oceans from the top-of-the-atmosphere energy imbalance. The following are the pertinent graphs without commentary, because the comments would basically be the same as those for the full oceans.
# # #
# # #
# # #
# # #
# # #
The energy imbalance at the top of the atmosphere and ocean heat accumulation are crucial elements in the hypothesis of human-induced global warming. Because there is no agreement among the climate models about the energy imbalance at the top of the atmosphere (Figure 1.24-1), there can be no agreement among the climate models about the heat accumulating in the oceans (Figures 1.24-7 and 1.24-14).
With the unlikely outliers removed, or referring to the GISS Model E2-R and GISS Model E2-H simulations, the differences between the observed and modeled ocean heat accumulation indicate the models are much too sensitive to the hypothetical impacts of CO2.
With the outliers removed, according to the model mean, the models are showing a heat accumulation that’s more than twice the observed heat accumulation for the depths of 0-700 meters and for the full oceans. And depending on the GISS climate model and depth, the modeled heat accumulation can be two to almost three times higher than what has been observed.
In the models used by the IPCC for their 5th Assessment Report, many of the modeled oceans are not storing heat close to the (much-adjusted) observed rates, so those models are not simulating global warming as it exists on Earth. But there’s really nothing new about that. We can simply add ocean heat accumulation and top-of-the-atmosphere energy imbalance to the list of things that climate models do not simulate properly: like surface temperatures, like precipitation, like polar sea ice, like polar amplification, like El Niño and La Niña processes, like the Atlantic Multidecadal Oscillation, like the Pacific Decadal Oscillation, and so on.
Once again, climate models have shown they are good for one thing and one thing only: to display how poorly they simulate Earth’s climate.
Important Note: As discussed, because of inherent flaws in climate models like model drift, when examining model performance, climate scientists will adjust the outputs of climate models before comparing them to data.
This chapter is based on my blog post Climate Models Fail: Global Ocean Heat Content (Based on TOA Energy Imbalance). As you’ll note in that post, I purposely used the wrong scaling factor for converting the top-of-the-atmosphere energy imbalance to ocean heat uptake. I did that because I found it odd that the trends of the observations aligned almost perfectly with the trends of the model mean for the full oceans and for the top 700 meters. While it’s likely only a coincidence, it appeared as though the models were tuned to the wrong scaling factor.
Many thanks to Willis Eschenbach and Roger Pielke, Sr. for their comments on the initial (but much different) drafts of that blog post and to researcher Nic Lewis for his comments on the thread of the cross post at WattsUpWithThat.
ABOUT THE ERRONEOUS TRENDS LISTED ON THE BOOK ILLUSTRATIONS
I noted above that this chapter was based on the blog post Climate Models Fail: Global Ocean Heat Content (Based on TOA Energy Imbalance), where I had purposely used the wrong scaling factor for converting the top-of-the-atmosphere energy imbalance to ocean heat uptake. If you were to compare the trends shown in that original blog post to the trends listed in Chapter 1.24 of my ebook On Global Warming and the Illusion of Control – Part 1, you’d note that I updated some of the trends in the book, but not all. My apologies.
The trends listed on the illustrations in this post fall into line with what we would expect when compared to the trends listed in the original post. That is, they are approximately 1.41 times higher.
NEXT IN THE SERIES
We’re going to take a look at the 8 outlying climate models presented in this post and discuss an important aspect of their outputs.