Monthly Archives: December 2009

New Paper On The Need For Improved Cloud Representation In Climate Models By Wang Et Al 2009

Yesterday, I discussed the issue that water vapor feedbacks are more poorly understood than indicated in the papers by Andrew Dessler (see). Today, I have provided a new paper that discusses one aspect of the current inability of the multi-decadal global climate models to skillfully predict cloud-precipitation feedbacks (and thus their difficulty in accurately representing radiative feedbacks in this models).

The new paper is

Wang, Y., C.N. Long, L.R. Leung, J. Dudhia, S.A. McFarlane, J.H. Mather, S.J. Ghan, and X. Liu. 2009. “Evaluating Regional Cloud-Permitting Simulations of the WRF Model for the Tropical Warm Pool International Cloud Experiment (TWP-ICE), Darwin, 2006.” J. Geophys. Res., 114, D21203, doi:10.1029/2009JD012729

The abstract reads

“Data from the Tropical Warm Pool International Cloud Experiment (TWP-ICE) were used to evaluate Weather Research and Forecasting (WRF) model simulations with foci on the performance of three six-class bulk microphysical parameterizations (BMPs). Before the comparison with data from TWP-ICE, a suite of WRF simulations were carried out under an idealized condition, in which the other physical parameterizations were turned off. The idealized simulations were intended to examine the interaction of BMP at a “cloud-resolving” scale (250 m) with the nonhydrostatic dynamic core of the WRF model. The other suite of nested WRF simulations was targeted on the objective analysis of TWP-ICE at a “cloud-permitting” scale (quasi-convective resolving, 4 km). Wide ranges of discrepancies exist among the three BMPs when compared with ground-based and satellite remote sensing retrievals for TWP-ICE. Although many processes and associated parameters may influence clouds, it is strongly believed that atmospheric processes fundamentally govern the cloud feedbacks through the interactions between the atmospheric circulations, cloudiness, and the radiative and latent heating of the atmosphere. Based on the idealized experiments, we suggest that the discrepancy is a result of the different treatment of ice-phase microphysical processes (e.g., cloud ice, snow, and graupel). Because of the turn-off of the radiation and other physical parameterizations, the cloud radiation feedback is not studied in idealized experiments. On the other hand, the “cloud-permitting” experiments engage all physical parameterizations in the WRF model so that the radiative heating processes are considered together with other physical processes. Common features between these two experiment suites indicate that the major discrepancies among the three BMPs are similar. This strongly suggests the importance of ice-phase microphysics. To isolate the influence of cloud radiation feedback, we further carried out an additional suite of simulations, which turns off the interactions between cloud and radiation schemes. It is found that the cloud radiation feedback plays a secondary, but nonnegligible role in contributing to the wide range of discrepancies among the three BMPs.”

There is a news release for this paper that is titled

Computer-simulated Thunderstorms with Ice Clouds Reveal Insights for Next-generation Computer Models

Excerpts from the paper are [highlight added]

“Thunderstorms in the tropics generate widespread cirrus clouds that are important in reflecting and absorbing energy. These mixing ratios for granular snow pellets (also called “soft hail”) (shades) and cloud ice (contours) from comparison testing of thunderstorm clouds in the tropics illustrate the wide discrepancy of the ice-phase cloud microphysics in current models. The melting line is marked as a thicker, red line. These types of discrepancies must be resolved for models to more accurately predict cloud influence on climate change.”

Computer simulations of thunderstorms using data from a field campaign in Australia confirm that the “ice-phase” cloud processes in climate models contribute most to the wide discrepancy between model results and actual cloud measurements. This was a key finding from PNNL scientist Dr. Yi Wang and his colleagues from a recent study.”

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New Paper “Temperature And Equivalent Temperature Over The United States (1979 – 2005) By Fall Et Al 2009

We have a new paper that documents the need to include water vapor trends, in addition to  temperature trends, in the assessment of climate system heat changes (which, of course, includes global warming). 

Our paper is

Fall, S., N. Diffenbaugh, D. Niyogi, R.A. Pielke Sr., and G. Rochon, 2009:  Temperature and equivalent temperature over the United States (1979 – 2005). Int. J. Climatol., in press

has been accepted. The abstract of our paper reads

Temperature (T) and equivalent temperature (TE) trends over the United States from 1979 to 2005 and their correlation to land cover types are investigated using National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data, the Advanced Very High Resolution Radiometer (AVHRR) land use/cover classification, the National Land Cover Database (NLCD) 1992-2001 Retrofit Land Cover Change, and the Normalized Difference Vegetation Index (NDVI) derived from AVHRR.

Even though most of the magnitude of TE is explained by T, the moisture component induces larger trends and variability of TE relative to T. The contrast between pronounced temporal and spatial differences between T and TE at the near-surface level and minor to no differences at 300 mb – 200 mb is a consistent pattern. This study therefore demonstrates that in addition to temperature, atmospheric heat content may help to quantify the differences between surface and tropospheric heating trends, and hence the impact of land cover types on the surface temperature changes.

Correlations of T and TE with NDVI reveal that TE shows a stronger relationship to vegetation cover than T, especially during the growing season, with values that are significantly different and of opposite signs (-0.31 for T vs. NDVI; 0.49 for TE vs. NDVI). Our results suggest that land cover types influence both moisture availability and temperature in the lower atmosphere and that TE is larger in areas with higher physical evaporation and transpiration rates. As a result, TE can be used as an additional metric for analyzing near-surface heating trends with respect to land cover types. Moreover, TE can be tested as a complementary variable for assessing the impact of land surface and boundary layer processes in reanalysis and weather/climate model studies.

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Comment From Josh Willis On The Upper Ocean Heat Content Data Posted On Real Climate

Real Climate has a post titled  Updates to model-data comparisons which includes a plot of the variations in upper ocean content anomalies from the period 1955 through 2009 .  

I asked Josh Willis the following with respect to the plot in the Real Climate post

My question:

Real Climate has posted a plot of ocean heat content, which we have
 discussed before, that shows a sudden jump in the 2002-2003 time frame;

 This jump is not seen it other metrics, including the surface temperatures
 (which they show) or the lower tropospheric temperatures (e.g. see

 see Figure 7 TLT

 Can you comment on the realism of this jump? Would you be willing to let me
 post your reply, if you do comment?

 Most of their trend agreement with the models is due to this single jump.

Josh Willis’s reply [reproduced with his permission]

There is still a good deal of uncertainty in observational estimates of ocean heat content during the 1990s and into the early part of the 2000s. This is because of known biases in the XBT data set, which are the dominant source of ocean temperature data up until 2003 or 2004. Numerous authors have attempted to correct these biases, but substantial difference remain in the “corrected” data.  As a result, the period from 1993 to 2003 still has uncertainties that are probably larger than the natural or anthropogenic signals in ocean heat content that happen over a period of 1 to 3 years.  However, the decadal trend of 10 to 15 years seems to be large enough to see despite the uncertainties. Because Argo begins to become the dominant source of temperature data in about 2004, the period from 2000 to 2005 is especially worriesome because of the transition from an XBT-dominated estimate of ocean heat content.

You might also comment that there is another easily available estimate besides that of Levitus et al. (the one shown in this blog entry).  The other long-term estimate is from Domingues et al. and can be downloaded from CSIRO:

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Q & A Are Water Vapor Feedbacks From Added CO2 Well Understood?

The issue of the relative roles of the human addition of CO2 and the resulting water vapor feedback remains an incompletely understood issue [and thanks to Tom Fuller for encouraging me to address this question].

As reported on Watts Up With That in a post titled NASA says AIRS satellite data shows positive water vapor feedback

 “AIRS temperature and water vapor observations have corroborated climate model predictions that the warming of our climate produced as carbon dioxide levels rise will be greatly exacerbated — in fact, more than doubled — by water vapor,” said Andrew Dessler, a climate scientist at Texas A&M University, College Station, Texas.

Dessler explained that most of the warming caused by carbon dioxide does not come directly from carbon dioxide, but from effects known as feedbacks. Water vapor is a particularly important feedback. As the climate warms, the atmosphere becomes more humid. Since water is a greenhouse gas, it serves as a powerful positive feedback to the climate system, amplifying the initial warming. AIRS measurements of water vapor reveal that water greatly amplifies warming caused by increased levels of carbon dioxide. Comparisons of AIRS data with models and re-analyses are in excellent agreement.

For an observational proof of strong water vapor feedback Dressler recommends

Dessler, A.E., and Sherwood, S.C. A matter of humidity, Science, 323, 1020-1021, DOI: 10.1126/science.1171264, 2009


Dessler, A.E., Zhang, Z, and Yang, P. The water-vapor climate feedback inferred from climate fluctuations, 2003-2008, Geophys. Res. Lett., 35, L20704, DOI: 10.1029/2008GL035333, 2008

where the abstract reads

Between 2003 and 2008, the global-average surface temperature of the Earth varied by 0.6°C. We analyze here the response of tropospheric water vapor to these variations. Height-resolved measurements of specific humidity (q) and relative humidity (RH) are obtained from NASA’s satellite-borne Atmospheric Infrared Sounder (AIRS). Over most of the troposphere, q increased with increasing global-average surface temperature, although some regions showed the opposite response. RH increased in some regions and decreased in others, with the global average remaining nearly constant at most altitudes. The water-vapor feedback implied by these observations is strongly positive, with an average magnitude of λ q = 2.04 W/m2/K, similar to that simulated by climate models. The magnitude is similar to that obtained if the atmosphere maintained constant RH everywhere,

while their conclusion is

“The existence of a strong and positive water-vapor feedback means that projected business-as-usual greenhouse gas emissions over the next century are virtually guaranteed to produce warming of several degrees Celsius. The only way that will not happen is if a strong, negative, and currently unknown feedback is discovered somewhere in our climate system.”

The interesting statement that ”

Dessler explained that most of the warming caused by carbon dioxide does not come directly from carbon dioxide, but from effects known as feedbacks. Water vapor is a particularly important feedback”

illustrates why skillful climate prediction is such a difficult science issue. Unlike direct radiative forcings, such as a volcanic eruption, in which the diabatic cooling in the troposphere and diabatic heating in the stratospheric can be straightforwardly diagnosed from the amount of ejecta,  the accurate long-term climate change from the human addition of CO2 is a much more complex problem.  The water vapor feedback also involves clouds and precipitation in which phase changes into liquid water and ice occur.

There is a new paper under review that illustrates the inability of the IPCC type models to skillfully predict the water vapor feedback  and thus raises questions on Dressler’s conclusion of model skill (e.g see also Roy Spencer’s research on this topic). This new paper is

Wu, C., T. Zhou, and D.-Z. Sun, 2009: Atmospheric Feedbacks over the Tropical Pacific in Observations and Atmospheric General Circulation Models: An Extended Assessment. J. Climate, Submitted.

The abstract of this paper reads

“The dynamical and radiative feedbacks from the deep convection over the tropical Pacific are quantified using ENSO signal in that region for both the observation and 16 climate models. Different from a previous analysis, we recognize the nonlinear relationship between deep convection and SST over that region, and perform the evaluation using the data from the warm phase and the cold phase separately. We also employ a much longer dataset than the previous analysis. While the results confirm the previous finding that most models underestimate the cloud albedo feedback and overestimate the water vapor feedback, we also show that the discrepancies mainly come from the warm phase, underscoring deep convection as a major source of error. In the cold phase, the models are found to have feedbacks of comparable magnitude and similar spatial pattern to the observations. Examination of the cause of the weaker feedback from cloud albedo in the models suggests that the bias is likely linked to a weaker relationship between the short-wave cloud forcing and the precipitation in the models. In addition, the analysis reveals a systematic feedback bias from the latent heat flux: the models tend to have a too strong positive feedback of latent heat flux over the central Pacific. The results suggest that the deficiency in the atmospheric feedbacks, particularly those from the deep convection, is a possible cause for the excessive cold-tongue in coupled models.”

Excerpts from the conclusion read

“…..our extended analysis further substantiates the suggestion that the excessive cold-tongue problem may have something to do with the weak regulating effect from the model atmosphere—the deep convection in particular. The analysis based on the data from the ENSO warm phase shows that all models — with no exception — have a net atmospheric feedback that is far weaker than that in the observation. While in the cold phase, some models replicate the observed ∂Fs/∂T feedback. Further more, this result underscores the relationship between the underestimate of feedbacks and deep convection.”

“……The results underscore the potentially critical role of deep convection in the large-scale tropical ocean-atmosphere interaction, and the continuing difficulty in capturing this role in the current state-of-the-art climate models.”

Other papers on this topic led by Dr. Sun include

Sun, D.-Z., Y. Yu, and T. Zhang, 2009: Tropical Water Vapor and Cloud Feedbacks in Climate Models: A Further Assessment Using Coupled Simulations. J. Climate, 22, 1287-1304.

Thus, while Andrew Dressler is correct that water vapor feedback is required to significantly amplify the warming effect of added CO2, the water vapor feedback itself is not as well understood as he has indicated. Moreover,  their conclusions indicate that any warming of the atmosphere, such as from black carbon (soot) would also have such a strong positive water vapor feedback.  The Dressler analysis should be generalized to include all positive and negative radiative forcings.

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Q & A On the Adequacy of the Upper Ocean Heat Content to Diagnosis Global Warming

In response to my posts 

Information on the Argo Ocean Monitoring Network 

Comment On EPA Response To Reviewer Comments On Ocean Heat Content 

Further Comments on The Inadequate EPA Response To Reviewer Comments On Ocean Heat Content 

I am providing further information as to why the upper ocean heat content has been adequately sampled particularly since 2005 [and thanks to Leonard Ornstein for encouraging me to do this!]. 

The direct sampling of vertical profiles in the ocean is completed by the Argo network

Spatial plot of Argo network

 The ocean data is less uncertain also because it does not have the larger spatial variations that the land part of the surface temperature. It also does not have much larger diurnal range that occurs on land; e.g. see the spatial distribution of the surface temperature portion of the upper ocean temperature anomalies for December 24 2009; 

Sea Surface Temperature Anomalies

and for the land for November 2009  from the NOAA Terra satellite (see; this website also animates the anomalies for each month back to 2005);  

Land surface temperature anomalies November 2009


The MODIS- TERRA sample, of course, is a snapshot of land at a particular time in the diurnal cycle, while the Argo network samples a climate component (the ocean) in which a large daily cycle of temperature does not occur.  

In addition, the upper ocean data is mass weighted with heat content expressed in Joules, while the surface temperatures are not. There is no lags or “heat in the pipeline” when Joules are used as the currency to diagnose the Earth’s radiative imbalance; e.g. see 

Is There Climate Heating In “The Pipeline”? 

Further Comments Regarding The Concept “Heating In The Pipeline” 

Thus the Argo network, along with other observational platforms including GRACE and satellite altimetry; e.g. see provides a much more robust methodology to monitor global warming than the NCDC, GISS and CRU surface temperature data analyses.

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A New Paper On The Role Of Biomass Burning On The Climate System – Tosca Et Al 2009

Papers which document human climate forcings other than CO2 continue to be published.

Today I was alerted to a paper that demonstrates the major role of biomass burning on regional climate. The paper is

M. G. Tosca, J. T. Randerson, C. S. Zender, M. G. Flanner, and P. J. Rasch, 2009: Do biomass burning aerosols intensify drought in equatorial Asia during El Nino? Manuscript prepared for Atmos. Chem. Phys. with version 3.0 of the LATEX class copernicus.cls. Date: 6 August 2009

The abstract reads

“During El Nino years, fires in tropical forests and peatlands in equatorial Asia create large regional smoke clouds. We characterized the sensitivity of these clouds to regional drought, and we investigated their effects on climate by using an atmospheric general circulation model. Satellite observations during 2000-2006 indicated that El Ni˜no-induced regional drought led to increases in fire emissions and, consequently, increases in aerosol optical depths over Sumatra, Borneo and the surrounding ocean. Next, we used the Community Atmosphere Model (CAM) to investigate how climate responded to this forcing. We conducted two 30 year simulations in which monthly fire emissions were prescribed for either a high (El Ni˜no; 1997) or low (La Ni˜na; 2000) fire year using a satellite derived time series of fire emissions. Our simulations included the direct and semi-direct effects of aerosols on the radiation budget within the model. Fire aerosols reduced net shortwave radiation at the surface during August–October by 19.1 ± 12.9 Wm−2 (10%) in a region that encompassed most of Sumatra and Borneo (90E–120E, 5S–5N). The reductions in net radiation cooled sea surface temperatures (SSTs) and land surface temperatures by 0.5 ± 0.3 and 0.4 ± 0.2C during these months. Tropospheric heating from black carbon (BC) absorption averaged 20.5 ± 9.3 Wm−2 and was balanced by a reduction in latent heating. The combination of decreased SSTs and increased atmospheric heating reduced regional precipitation by 0.9 ± 0.6 mmd−1 (10%). The vulnerability of ecosystems to fire was enhanced because the decreases in precipitation exceeded those for evapotranspiration. Together, the satellite and modeling results imply a possible positive feedback loop in which anthropogenic burning in the region intensifies drought stress during El Nino.”

The finding that the tropospheric heating from black carbon absorption averaged 20.5 ± 9.3 Wm−2  is on the same order of the heating rates that we found in our paper

Matsui, T., and R.A. Pielke Sr., 2006: Measurement-based estimation of the spatial gradient of aerosol radiative forcing. Geophys. Res. Letts., 33, L11813, doi:10.1029/2006GL025974.

As we wrote in our paper

“….. the spatial mean and the spatial gradient of the aerosol radiative forcing in comparison with
those of well-mixed green-house gases (GHG). Unlike GHG, aerosols have much greater spatial heterogeneity in their radiative forcing. The heterogeneous diabatic heating can modulate the gradient in horizontal pressure field and atmospheric circulations, thus altering the regional climate.”

The new Tosca et al 2009 further confirms our conclusion on the major role of human aerosols in altering regional climate.

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Yet Another Human Climate Warming Effect In The Arctic – Aircraft Contrails

We have reported on the role of black carbon (soot) as a major non-greenhouse gas human climate forcing in the Arctic; e.g. see

New Study On The Role Of Soot Within the Climate In The Higher Latitudes And On “Global Warming

where an article in Scientific American by David Biello based on a study by Charlie Zender, a climate physicist at the University of California, Irvine stated

“…. on snow—even at concentrations below five parts per billion—such dark carbon triggers melting, and may be responsible for as much as 94 percent of Arctic warming”.

Now we have yet another human climate forcing that was reported by Rex Dalton  of Nature News in the article

How aircraft emissions contribute to warming – Aviation contributes up to one-fifth of warming in some areas of the Arctic.

The article includes the text

“The first analysis of emissions from commercial airline flights shows that they are responsible for 4–8% of surface global warming since surface air temperature records began in 1850 — equivalent to a temperature increase of 0.03–0.06 °C overall.

The analysis, by atmospheric scientists at Stanford University in Palo Alto, California, also shows that in the Arctic, aircraft vapour trails produced 15–20% of warming.”

The photo in the news release has the caption

“Aircraft emissions could be having a dramatic effect on the warming of the Arctic”.

Clearly, as we summarized in our EOS article

Pielke Sr., R., K. Beven, G. Brasseur, J. Calvert, M. Chahine, R. Dickerson, D. Entekhabi, E. Foufoula-Georgiou, H. Gupta, V. Gupta, W. Krajewski, E. Philip Krider, W. K.M. Lau, J. McDonnell,  W. Rossow,  J. Schaake, J. Smith, S. Sorooshian,  and E. Wood, 2009: Climate change: The need to consider human forcings besides greenhouse gases. Eos, Vol. 90, No. 45, 10 November 2009, 413. Copyright (2009) American Geophysical Union

the human role in the climate system is much more than the human emissions of  CO2 and a few other greenhouse gases.

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Further Comments on The Inadequate EPA Response To Reviewer Comments On Ocean Heat Content

UPDATE: Decmber 28 2009: The source of the descrepancy between the 0.77 Watts per meter squared reported by von Schuckmann et al and the 0.47 Watts per meter squared that I calculated is that my value distributed this heating over the global area [thanks to David Douglas and Robert Knox for solving this decrepancy!]. I have updated the text below as needed.  There is the question as to whether the same flux of heat that enters the ocean should be assumed to be the large majority of heating  of the climate system or if the same flux of heat occurs over land and in the melting of sea and continental ice.  

In my post Comment On EPA Response To Reviewer Comments On Ocean Heat Content, the EPA presented the paper

von Schuckmann, K., F. Gaillard, and P.-Y. Le Traon (2009), Global hydrographic variability patterns during 2003–2008, J. Geophys. Res., 114, C09007, doi:10.1029/2008JC005237

as refuting the claims that the climate system is not accumulating heat.  As I report in this post, the EPA response is in error [thanks to Ron Cram and Leonard Ornstein for encouraging me to write this post]

The EPA reported

“…..(von Schuckmann et al., 2009) indicates the global ocean accumulated (between the surface and 2,000 meter depth) 0.77 (plus or minus 0.11) watts per square meter of heat between 2003 and 2008, which is roughly consistent with the 0.86 (plus or minus 0.12) watts per square meter of heat (between the surface and 750 meter depth) accumulated between 1993 and 2003 as documented in Willis et al. (2004); and Hansen et al. (2005). These studies suggest the ocean has and continues to accumulate heat, contributing to an overall imbalance in the Earth’s energy budget.”

The abstract of the von Schuckmann et al 2009 paper reads

“Monthly gridded global temperature and salinity fields from the near-surface layer down to 2000 m depth based on Argo measurements are used to analyze large-scale variability patterns on annual to interannual time scales during the years 2003–2008. Previous estimates of global hydrographic fluctuations have been derived using different data sets, partly on the basis of scarce sampling. The substantial advantage of this study includes a detailed summary of global variability patterns based on a single and more uniform database. In the upper 400 m, regions of strong seasonal salinity changes differ from regions of strong seasonal temperature changes, and large amplitudes of seasonal salinity are observed in the upper tropical and subpolar global ocean. Strong interannual and decadal changes superimpose long-term changes at northern midlatitudes. In the subtropical and tropical basin, interannual fluctuations dominate the upper 500 m depth. At southern midlatitudes, hydrographic changes occur on interannual and decadal time scales, while long-term changes are predominantly observed in the salinity field. Global mean heat content and steric height changes are clearly associated with a positive trend during the 6 years of measurements. The global 6-year trend of steric height deduced from in situ measurements explains 40% of the satellite-derived quantities. The global freshwater content does not show a significant trend and is dominated by interannual variability.

The figure  (Figure 11 top) which was presented in their paper to document this heating rate in the ocean from the surface to 2000m is reproduced below.


I have tried to reproduce the linear trend that is plotted on this figure (of 0.77 Watts per meter squared), but cannot reproduce the value (I have requested clarification from von Schuckmann but there, as of yet, has been no reply).

My calculation is presented below, and I welcome e-mails if you can find the source of the discrepancy [or any errors in my calculations]. The units in the above figure are in Joules per meter squared. To convert to total Joules, the value in Joules per meter squared are multiplied by the area of the ocean [3.35 x 108 kilometers squared].  Thus, for example, 1 x 108Joules per meter squared  becomes 3.35 x 1022 Joules [0.56 x 1022 Joules per year if this heat accumulates over 6 years]. 

Since, 0.6 Watts per meter squared is equal to 0.98  x 1022 Joules per year when the heat is distributed globally (see), 0.56 x 1022 Joules per year is equal to 0.34 Watts per meter squared. From the above figure, the change of heat content for the 6 years is estimated as about 1.4 x 108 Joules per meter squared [4.69 x 1022 Joules] which is equal to 0.47 Watts per meter squared.  This is 0.30 Watts per meter squared (39%)  less than what is presented in the von Schuckmann et al Figure due to how the heating is distributed globally.

In order to further analyze the data, we can estimate the heating rate for each year from the above figure;

From the von Schuckmann et al  Figure 11 (top) of the heat accumulation in the ocean layer 0 to 2000m

2003   +0.5 x 108 Joules per meter squared   

2004     0.0 x 108 Joules per meter squared

2005    +0.5  x 108 Joules per meter squared

2006     0.0  x 108 Joules per meter squared

2007     +0.4  x 108  Joules per meter squared

2008      0.0 x 108  Joules per meter squared

Total over the 6 years  is about 1.4 Joules per meter squared which yields 4.69 x 1022 Joules.

The task now is to compare with the models. In their paper

Hansen, J., L. Nazarenko, R. Ruedy, Mki. Sato, J. Willis, A. Del Genio, D. Koch, A. Lacis, K. Lo, S. Menon, T. Novakov, Ju. Perlwitz, G. Russell, G.A. Schmidt, and N. Tausnev, 2005: Earth’s energy imbalance: Confirmation and implications. Science, 308, 1431-1435, doi:10.1126/science.1110252,

they wrote

“Our climate model, driven mainly by increasing human-made greenhouse gases and aerosols among other forcings, calculates that Earth is now absorbing 0.85±0.15 W/m2 more energy from the Sun than it is emitting to space. This imbalance is confirmed by precise measurements of increasing ocean heat content over the past 10 years.” 

In the response by Jim Hansen to a comment by Christy and Pielke Sr   Hansen wrote me with respect to their GISS model predictions that

“Our simulated 1993-2003 heat storage rate was 0.6 W/m2 in the upper 750 m of the ocean.”

He further writes

“The decadal mean planetary energy imbalance, 0.75 W/m2, includes heat storage in the deeper ocean and energy used to melt ice and warm the air and land. 0.85 W/m2 is the imbalance at the end of the decade.”

Thus, the best estimate value of 0.60 Watts per meter squared given in Hansen et al. can be used as a conservative lower estimate [since it is just for the upper 750m while the von Schuckmann et al analysis includes the depths down to 2000m]  to compare with each other.

The observed best estimates of the observed heating and the Hansen et al. prediction in Joules in the upper 700 m of the ocean are given below:   

HANSEN PREDICTION OF THE ACCUMULATION OF JOULES [ at a rate of 0.60 Watts per meter squared] assuming a baseline of zero at the end of 2002 for the upper 750m of the oceans].

2003 ~0.98 x 1022Joules
2004 ~1.96 x1022Joules
2005 ~2.94 x1022Joules
2006 ~3.92 x1022Joules
2007 ~4.90 x 1022Joules
2008 ~5.88 x1022Joules
2009 ~6.86 x1022Joules
2010 ~7.84 x1022Joules
2011 ~8.82 x 1022Joules
2012 ~9.80 x 1022Joules

Thus, according to the GISS model predictions, there should be approximately 5.88 * 1022 Joules more heat in the upper 700 meters of the global ocean at the end of 2008 than were present at the beginning of 2003.

The von Schuckmann et al 2009 paper, using their value of 0.77 Watts per meter squared for the entire 2000 depth, shows an accumulation of 7.2 x 1022  Joules from 2003 to 2008 (a rate of 1.26 x 1022 Joules per year) which is in good agreement with the Hansen prediction.

However,  using the rate 0.47 Watts per meter squared diagnosed from the von Schuckmann et al 2009 paper gives an accumulation of 4.69 x 1022 Joules. For this estimate to come into agreement with the GISS model prediction by the end of 2012, for example, there would have to be an accumulation 5.1 x 1022Joules of heat over just the next three years. This requires a heating rate over the next 3 years into the upper 700 meters of the ocean of 1.70 x 1022Joules per year, which corresponds to a radiative imbalance of ~+1.0 Watts per square meter.

Since von Schuckmann et al examined a deeper layer, the actual heating  to compare with the GISS model predictions of heating in the upper 750m would have to be, conservatively, about 10% larger if we split the o.15 Watts per meter squared added heating in the deeper ocean, from melted ice, and input into the land and atmosphere that Jim Hansen listed in his e-mail to me. 

There is also another issue. How accurate are the estimates of heating in the earlier period of the von Schuckmann et al analysis? In an e-mail from Josh Willis of JPL, he writes

“The Agro Science Team continues to improve the accuracy of the float pressure data for the entire historical Argo dataset.  As it strives to achieve the array-averaged accuracy of 1-2 db that is necessary for estimates of global sea level and ocean heat content, small but significant revisions in estimates based on Argo should be expected, partiularly in the early years of the array prior to 2005.”

There is a paper which supports his finding, as well as provides further confidence in the analysis of Josh Willis. It is

Leuliette, E. W., and L. Miller (2009), Closing the sea level rise budget with altimetry, Argo, and GRACE, Geophys. Res. Lett., 36, L04608, doi:10.1029/2008GL036010.

Figure 2 from that paper is reproduced below which shows the close agreement between the Leuliette and Miller analysis and the Willis et al analyses, except for the period prior to 2005 [note: the steric sea level change is that part of sea level change from thermal expansion and salinity changes;  globally averaged the salinty changes must be small on this time period].

This figure also clearly documents that since 2005 there has not been the 0.77 Watts per meter squared heating that was claimed in the EPA Response.  Moreover, even the von Schuckmann et al analysis does not support this large of a heating rate.

The EPA failed to critically assess their response, and have misled policymakers on the actual rate of global warming.

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Effective Detective Work By John Nielsen-Gammon On The Error In The IPCC Report On Himalayan Glacier Retreat

UPDATE JANUARY 16 2010: See further information on this at [thanks to Charles Martin for alerting us to this!]

John Nielsen-Gammon has published an effective summary and further detailed analysis of the error Madhav Khandkkar reported on in a guest weblog Global Warming And Glacier Melt-Down Debate: A Tempest In A Teapot?” – A Guest Weblog By Madhav L Khandekar.

John’s post is titled By the way, there will still be glaciers in the Himalayas in 2035.

Excerpts from John’s detective work include

“Lost amid the news coverage of Copenhagen and Climategate was the assertion that one of the more attention-grabbing statements of the IPCC AR4 was flat-out wrong: [the IPCC text is]

Glaciers in the Himalaya are receding faster than in any other part of the world (see Table 10.9) and, if the present rate continues, the likelihood of them disappearing by the year 2035 and perhaps sooner is very high if the Earth keeps warming at the current rate. Its total area will likely shrink from the present 500,000 to 100,000 km2 by the year 2035 (WWF, 2005).”(IPCC AR4 WG2 Ch10, p. 493).”

“To recap, the available evidence indicates that the IPCC authors of this section relied upon a secondhand, unreferreed source which turned out to be unreliable, and failed to identify this source.  As a result, the IPCC has predicted the likely loss of most or all of Himalaya’s glaciers by 2035 with apparently no peer-reviewed scientific studies to justify such a prediction and at least one scientific study (Kotlyakov) saying that such a disappearance is too fast by a factor of ten!”

The entire post by John is worth reading.

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Q & A – The Role Of Landscape Change and Other Human Climate Forcings Within the Climate System

I have been asked two very interesting and important questions by Bill DiPuccio which I paraphrase and  summarize below:

1. What is the area of land that is directly modified by human landscape management? While it is clear that local and regional effects on climate will result, are hemispheric and global atmospheric circulations signficantly affected?

2. Is anthropogenic forcing “driving” (controlling) climate change or is it “modulating” (modifying) natural variability?

In answer to the first question,  in my paper

Pielke Sr., R.A., 2002: Overlooked issues in the U.S. National Climate and IPCC assessments. Climatic Change, 52, 1-11

I reported

“Actual landscape change [of the Earth’s land surface] has been estimated to be as high as 45% by Vitousek et al. (1997).”

Figure 3 in this paper shows how land use change has accelerated during the last 100 years. Another paper which investigates this issue is

Nemani, R.R., S.W. Running, R.A. Pielke, and T.N. Chase, 1996: Global vegetation cover changes from coarse resolution satellite data. J. Geophys. Res., 101, 7157-7162.

There have been model studies which show that these changes significantly alter hemispheric and global circulation patterns, e.g. see

Feddema et al. 2005: The importance of land-cover change in simulating future climates., 310, 1674-1678

Chase, T.N., R.A. Pielke, T.G.F. Kittel, R.R. Nemani, and S.W. Running, 2000: Simulated impacts of historical land cover changes on global climate in northern winter. Climate Dynamics, 16, 93-105.


Pielke Sr., R.A., G. Marland, R.A. Betts, T.N. Chase, J.L. Eastman, J.O. Niles, D. Niyogi, and S. Running, 2002: The influence of land-use change and landscape dynamics on the climate system- relevance to climate change policy beyond the radiative effect of greenhouse gases. Phil. Trans. A. Special Theme Issue, 360, 1705-1719

but this conclusion still remains debated; see

Pitman, A.J., N. de Noblet-Ducoudré, F.T. Cruz, E.L. Davin, G.B. Bonan, V. Brovkin, M. Claussen, C. Delire, L. Ganzeveld, V. Gayler, B.J.J.M. van den Hurk, P.J. Lawrence, M.K. van der Molen, C. Müller, C.H. Reick, S.I. Seneviratne, B. J. Strengers, and A. Voldoire, 2009: Uncertainties in climate responses to past land cover change: first results from the LUCID intercomparison study, Geophys. Res. Lett., doi:10.1029/2009GL039076, in press.

With respect to the second question,  this is very effectively summarized by Bill. He writes that the answer to the second question

” ….has to do with the degree of dominance. Is anthropogenic forcing “driving” (controlling) climate change or is it “modulating” (modifying) natural variability?  This is a fuzzy question and probably creates a false dilemma because it depends on the temporal and spatial scale.  But a nuanced answer is important not only to skeptics who emphasize natural variability, but also to policy makers who think that since humans control climate change, we can use geoengineering to “save the planet.”

My view is that humans modulate natural variability. This perspective is captured in the hypothesis in our EOS article

Pielke Sr., R., K. Beven, G. Brasseur, J. Calvert, M. Chahine, R. Dickerson, D. Entekhabi, E. Foufoula-Georgiou, H. Gupta, V. Gupta, W. Krajewski, E. Philip Krider, W. K.M. Lau, J. McDonnell,  W. Rossow,  J. Schaake, J. Smith, S. Sorooshian,  and E. Wood, 2009: Climate change: The need to consider human forcings besides greenhouse gases. Eos, Vol. 90, No. 45, 10 November 2009, 413. Copyright (2009) American Geophysical Union

where we wrote

“Although the natural causes of climate variations and changes are undoubtedly important, the human influences
are significant and involve a diverse range of first- order climate forcings, including, but not limited to, the human input of carbon dioxide (CO2)”


“In addition to greenhouse gas emissions, other first- order human climate forcings are important to understanding the future behavior of Earth’s climate. These forcings are spatially heterogeneous and include the effect of aerosols on clouds and associated precipitation [e.g., Rosenfeld et al., 2008], the influence of aerosol deposition (e.g., black carbon (soot) [Flanner et al. 2007] and reactive nitrogen [Galloway et al., 2004]), and the role of changes in land use/land cover [e.g., Takata et al., 2009]. Among their effects is their role in altering atmospheric and ocean circulation features away from what they would be in the natural climate system [NRC, 2005].”

Since the climate system is affected by a diverse range of human climate forcings as well as natural variability and change, we concluded in our EOS article

“We therefore propose that one should not rely solely on prediction as the primary policy approach to assess the potential impact of future regional and global climate variability and change. Instead, we suggest that integrated assessments within the framework of vulnerability, with an emphasis on risk assessment and disaster prevention, offer a complementary approach [Kabat et al., 2004]. This should be conducted in parallel with attempts to improve skill in predicting regional and global climate on multidecadal time scales. This leads to a practical and sensible way forward that will permit a more effective climate policy by focusing on the assessment of adaptation and mitigation strategies that can reduce the vulnerability of all of our important societal and environmental resources (involving water, food, energy, and human and ecosystem health) to both natural and human- caused climate variability and change”


“We recommend that the next assessment phase of the IPCC (and other such assessments) broaden its perspective to include all of the human climate forcings. It should also adopt a complementary and precautionary resource- based assessment of the vulnerability of critical resources (those affecting water, food, energy, and human and ecosystem health) to environmental variability and change of all types. This should include, but not be limited to, the effects due to all of the natural and human caused climate variations and changes.”

Thank you Bill for these two excellent questions!

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