Climate Science: Roger Pielke Sr. Research Group News

We all know that skillful weather prediction is very difficult, and after a week or so, little or no skill remains. Yet, the IPCC made, and policymakers are accepting, forecasts of climate decades from now as skillful. 

Part of this confusion is due to the fact that even professional societies are presenting conflicting definitions. For example, the American Meteorological Society (AMS) definition of “climate change” is

“(Also called climatic change.) Any systematic change in the long-term statistics of climate elements (such as temperature, pressure, or winds) sustained over several decades or longer. Climate change may be due to natural external forcings, such as changes in solar emission or slow changes in the earth’s orbital elements; natural internal processes of the climate system; or anthropogenic forcing.”

yet the same AMS defines the climate system as the

“system, consisting of the atmosphere, hydrosphere, lithosphere, and biosphere, determining the earth’s climate as the result of mutual interactions and responses to external influences (forcing). Physical, chemical, and biological processes are involved in the interactions among the components of the climate system.”

This later definition fits with the 2005 National Research Council  definition of the climate system as shown in the figure below from that report.

Using this figure, it is clear that short-term (up to a week or so) weather prediction is a subset of the atmosphere part of the above the figure.  Seasonal weather prediction is also a subset of this figure, where ocean and land components are required, along with the atmosphere, for skillful predictions.  Even in this case, aspects of these components, such as sea surface temperature, are usually assumed to be static over the time period of the prediction.

However, for multi-decadal climate predictions, all components of the climate system must be predicted. Even for decadal-long average values of climate variables (such as for a global average surface temperature)  an understanding of all of the natural and human climate forcings and feedbacks is still required.

Thus, in answer to the question on this weblog, weather is a subset of climate. Climate prediction is necessarily much more difficult than weather prediction. While the sensitivity of the climate system can be estimated from models by changing one or more components of the climate forcings (e.g., by inserting more CO₂ into the atmosphere), such as completed for the 2007 IPCC report, these are not climate predictions. To use the results of the IPCC models for regional assessments of climate impacts, such as the claim of more droughts in the future  (e.g., see and see), is scientifically flawed and thus is misleading policymakers.

With respect to the question raised by Climate Science on Figure SPM.2 in the IPCC Statement for Policymakers (see), I have decided to elevate the last comment from Gavin Schmidt (from Real Climate) and the Climate Science response to a weblog (with a couple of minor edits), as the issue that Climate Science is raising is not being clearly understood. Climate Science will also present a more detailed weblog on this subject soon in order to further clarify.

Real Climate Comment:

I’m still not following you. RC doesn’t have an ‘estimate’ - if asked, most of us would probably point to the IPCC values in the figure you highlight. I often use the GISS numbers (which are a little more complete, have some accounting of indirect effects and efficacy factors and are available as a time series), but there isn’t much in it. Your point about spinning the caption is completely obtuse - there is no contradiction as far as I can see. If you want to convince people that there is, you need to be more specific about what you mean. - gavin]

Climate Science Reply:

Gavin - Thank you for engaging in this discussion

If this is still not clear (and we remain in disagreement that this is a significant issue) I will write a more detailed weblog on Climate Science on this subject. The request that I have is to provide us with an estimate of the movement towards radiative balance of each of the forcings in Figure SPM.2.

Additional Real Climate Response:

I think I see the problem. You are using the term ‘radiative forcing’ to mean the current radiative imbalance. However, this usage appears to be unique to you. Radiative forcing in the IPCC sense and in most of the literature is a diagnostic of the external inputs to the climate system, not a measure of the result. It was designed to provide predictability of a model response at equilibrium after the system responds to an external perturbation. Therefore any use of the term ‘radiative forcing’ in the IPCC report refers to that, not the current imbalance. These are of course related: given an instantaneous forcing, the system will have an initial imbalance of the same magnitude, but as the system adjusts the imbalance will decrease. The key concept is that radiative forcing is by definition referenced to a previous state. In IPCC, that state is defined as 1750 conditions, but it would be fine to define the forcing with respect to any other period you want. The current imbalance is simply an instantaneous number.

Getting back to IPCC, there is still no problem with figure SPM-2 - it is a diagnosis of all the forcing elements and their uncertainty since the pre-industrial and is in a section entitled ‘Drivers of climate change’. The current imbalance however, cannot be apportioned based on forcing factor, it’s just one number. We’ve discussed estimates of that based on ocean heat content changes or what the models say previously. But if that is what you want to discuss, why didn’t you just say so initially? Redefining terms unilaterally just leads to confusion. - gavin]

Climate Science Reply

I am using the definition from

National Research Council, 2005: Radiative forcing of climate change: Expanding the concept and addressing uncertainties, where on page 13 it is defined as

“A climate forcing is an energy imbalance imposed on the climate system either externally or by human activities. Examples include changes in solar energy output, volcanic emissions, deliberate land modification, or anthropogenic emissions of greenhouse gases, aerosols, and their precursors.” [from http://www.nap.edu/openbook.php?record_id=11175&page=13].

I am defining that “the term ‘radiative forcing’ (i.e. the climate forcing) means the current radiative imbalance, but this consistent with the National Research Council report of which at least one Real Climate author participated in.

Further Reply From Real Climate

I disagree. The NRC definition is fine (but the version on p15 is more complete) but your paraphrase is not (the problem is that ‘imposed imbalance’ is not the ‘current imbalance’). But now that we have sorted out the semantic confusion, what is it that you really want to discuss? - gavin]

Climate Science Response

Gavin - What I am requesting is an estimate of the fraction of the current radiative imbalance associated with each climate forcing.

Follow Up Comment On Stoat (by James Annan)

“I think RP is really asking about the current radiative imbalance: while I do not think it is wrong or misleading to talk about total forcing (with a 1750 baseline) as the IPCC do, the other question is also interesting as it relates directly to warming ‘in the pipeline’. Of course the answer is we do not know for sure, since it directly depends on the climate sensitivity (and even the effective climate sensitivity of the current climate state, which may be slightly different again). But a rough ballpark estimate would be that a little more than half of the total forcing (IPCC terminology) remains as a current imbalance (the “commitment” runs in the AR4 show the future warming due to this imbalance). Of course splitting this up further into the contribution of each component would then become rather arbitrary”.

Two Further Comments From Real Climate

1. RP earlier:
> The request that I have is to provide us
> with an estimate of the movement towards
> radiative balance of each of the forcings

Comment by Hank Roberts

2. I don’t think it can be done robustly. A straight-forward apportioning based on the fractional contribution to the original forcing neglects the differing transient behaviour. For instance if one forcing agent rose quickly and stabilised, while another increased later, then the impact of each on the current imbalance should be weighted towards the latter. So that’s no good. Maybe you could do it by examining the single forcing transient runs we did for our recent paper (table 1) and looking at the year 2000-2003 (say) imbalances in Ann/Net TOA radiation. You’d need to check that the individual components do in fact add up to something close to the combined effect (not obviously true). However, different models might give quite different results, and you can only do this for forcings we’ve run. Other groups didn’t do as many single forcing experiments and so you might not be able to find another set of numbers to compare with. Attribution requires models however, and so I don’t see how you could do it any other way. - gavin]

Climate Science Reply

Hank - Please read the papers

I plan to weblog on this subject further in a new weblog on Climate Science. Thank you Gavin, Hank and others for your feedback.

Climate Science has asked the questions below in a comment on the weblog Real Climate. Their answer will be posted here also.

Comment on Real Climate:

Climate Science has a question for Real Climate. The 2007 IPCC Statement for Policymakers [Figure SPM.2] has the following caption

“Global average radiative forcing (RF) estimates and ranges in 2005 for anthropogenic carbon dioxide (CO2 ), methane (CH4 ), nitrous oxide (N2O) and other important agents and mechanisms, together with the typical geographical extent (spatial scale) of the forcing and the assessed level of scientific understanding (LOSU).”

but also the footnote on page 2 that

“Radiative forcing is a measure of the influence that a factor has in altering the balance of incoming and outgoing energy in the Earth-atmosphere system and is an index of the importance of the factor as a potential climate change mechanism. Positive forcing tends to warm the surface while negative forcing tends to cool it. In this report, radiative forcing values are for 2005 relative to pre-industrial conditions defined at 1750 and are expressed in watts per square metre (W m–2)…..”

Which of the two are correct?

Assuming that you agree that the footnote is correct, and the figure caption is in error, what is the Real Climate estimate in Watts per meter squared in 2005 (or in 2007) of the radiative forcing components and range for a figure analogous to Figure SPM.2 in the Statement for Policymakers?

Follow Up #1: Real Climate (Gavin Schmidt) replied on their weblog

[Response: I fail to see how you are parsing this to find an inconsistency. The footnote is clear that the term ‘radiative forcing’ in the IPCC report refers to the change in forcing from a 1750 baseline. More precisely, it is defined as the change in radiation at the tropopause after stratospheric temperature adjustment but with all other factors kept fixed when going from 1750 conditions to a new value. The caption to the figure discusses the radiative forcing (which remember is defined relative to 1750) in 2005. i.e. the forcing calculated in going from 1750 conditions to 2005. What is the problem? - gavin]

My Reply is

“Gavin - You have avoided answering the question (the issue that you are spinning an error in the figure caption should be obvious to most). The more important issue, which you have not addressed is

‘What is the Real Climate estimate in Watts per meter squared in 2005 (or in 2007) of the radiative forcing components and range for a figure analogous to Figure SPM.2 in the Statement for Policymakers?’”

Follow Up #2 - William M. Connolley posted on Stoat the following weblog [The case of the vanishing post] on my question to Real Climate:

“According to google reader, RP Sr posted the below today to his “blog” (only its not really a blog cos it doesn’t allow comments), in a post entitled Question The Weblog Real Climate. And indeed he did; the comment is here. This is something RP has been harping on about for a while. Gavin gave him the obvious answer: I fail to see how you are parsing this to find an inconsistency. The footnote is clear that the term ‘radiative forcing’ in the IPCC report refers to the change in forcing from a 1750 baseline. More precisely, it is defined as the change in radiation at the tropopause after stratospheric temperature adjustment but with all other factors kept fixed when going from 1750 conditions to a new value. The caption to the figure discusses the radiative forcing (which remember is defined relative to 1750) in 2005. i.e. the forcing calculated in going from 1750 conditions to 2005. What is the problem? Oddly, though, RP *hasn’t* posted Gavins response but seems to have deleted the entire misconceived post. Anyway, here is RP’s question”:

[then my weblog followed which I will not repeat here, since it is listed above]

My Reply

“William - Thank you for posting on your weblog. Gavin did not adequately answer the question before, so I am trying again.

Gavin can seek to “spin” the figure caption, but it is clearly an error which is misleading to policymakers.”

Follow Up #3: Further Reply From Real climate

” [Response: I’m still not following you. RC doesn’t have an ‘estimate’ - if asked, most of us would probably point to the IPCC values in the figure you highlight. I often use the GISS numbers (which are a little more complete, have some accounting of indirect effects and efficacy factors and are available as a time series), but there isn’t much in it. Your point about spinning the caption is completely obtuse - there is no contradiction as far as I can see. If you want to convince people that there is, you need to be more specific about what you mean. - gavin]

My Reply

Gavin - Thank you for engaging in this discussion

I agree that the figure in the IPCC Figure is the forcing differences for each climate forcing for pre-industrial to 2005. However, the caption states that this is the “Global average radiative forcing (RF) estimates and ranges in 2005″. The implication in this statement is that the climate forcings in figure SPM.2 are the current radiative forcing.

The reason that this is important is that the climate system has warmed since preindustrial times such that the radiative forcing is smaller since there has been some adjustment towards equilibrium. The time history of these forcings is different so their contributions each have had different time periods to result in some adjustment.

For example, if the difference of a climate forcing since preindustrial were 1 Watt per meter squared, some of this radiative imbalance would be adjusted for by warming since preindustrial time. The current radiative forcing for this example, therefore, would be less than 1 Watt per meter squared. If there were no adjustment (i.e. no increase in outgoing long wave irradiance from the Earth), of course, the current radiative imbalance from this example would be 1 Watt per meter squared. Figure SPM.2, therefore, does not present the current radiative forcing, but this is what is needed to describe the current state of the global average radiative forcing.

A simple example further illustrates. When you heat a pot of cold water, the heating is in imbalance until the water warms enough such that its heat loss is equal to the heat input from the stove. The heat imbalance at any time after the burners are started is less than the difference in heat between the time before the burners were turned on and any subsequent time. Eventually, the heat imbalance (i.e. analogous to the radiative forcing) becomes zero. It is the current imbalance in the radiative forcings that should be plotted (estimated in a figure of the form given in Figure SPM.2).

If this is still not clear (and we remain in disagreement that this is a significant issue) I will write a more detailed weblog on Climate Science on this subject. The request that I have is to provide us with an estimate of the movement towards radiative balance of each of the forcings in Figure SPM.2.

Climate Science has been posting weblogs since July 2005. This weblog highlights the topic areas and the link to each category.

Climate Change Forcings and Feedbacks

Climate Change Metrics

Climate Models

Climate Science Meetings

Climate Science Misconceptions

Climate Science Op-Eds

Climate Science Reporting

Definition of Climate

Guest Weblogs

Q&A on Climate Science

Vulnerability Paradigm

The Major Conclusions of Climate Science

This is a long weblog. The bottom line conclusions are written here to motivate reading the entire weblog.

CONCLUSIONS:

1. The primary focus on carbon dioxide inappropriately deemphasizes the first order importance of the other climate system heat system forcings (both cooling and warming forcings), as well as does not address the spatially complex, and incompletely understood, actual pattern of global climate system heat changes.

2. Attempts to significantly influence regional and local-scale climate based on controlling CO2 emissions alone is an inadequate policy for this purpose.

A starting point for the assessment of the relative fraction of global warming that is attributable to the radiative forcing of CO2 is the Summary Figure from the 2002 IPCC report (see). Clearly, according to their analysis, in comparing the change of radiative forcing since pre-industrial times until the present as estimated from the IPCC summary figure, the well-mixed greenhouse gases dominate the forcings which cause warming (about a 2.4 Watts per meter squared difference between these two time periods), of which about 1.4 Watts per meter squared is from CO2. Other warming forcings that they include, if the mean value plotted is used, are black carbon from burning fossil fuels (about 0.2 Watts per meter squared), tropospheric ozone (about 0.3 Watts per meter squared), and solar (about 0.25 Watts per meter squared).

Using these values about 58% of the radiative forcing of the well-mixed greenhouse gases results from CO2, and about 48% of the warming human-caused climate forcings result from the radiative forcing of CO2.

The following extracts from research studies reduce the relative contribution of the radiative forcing of CO2 as reported in the 2002 IPCC Report, as summarized above. These studies report the following,

“NASA scientists have found that a major form of global air pollution involved in summertime “smog�? has also played a significant role in warming the Arctic……According to this new research, ozone was responsible for one-third to half of the observed warming trend in the Arctic during winter and spring. Ozone is transported from the industrialized countries in the Northern Hemisphere to the Arctic quite efficiently during these seasons. �?
(see).

�?Even within the well-mixed greenhouse gas forcings, there are new complications. Drew Shindell and colleagues, as reported in Pollution Online found that, ’According to new calculations, the impacts of methane on climate warming may be double the standard amount attributed to the gas. The new interpretations reveal methane emissions may account for a third of the climate warming from well-mixed greenhouse gases between the 1750s and today. The IPCC report, which calculates methane’s affects once it exists in the atmosphere, states that methane increases in our atmosphere account for only about one sixth of the total effect of well-mixed greenhouse gases on warming. ’�? (see).

Moreover, from the 2006 Nature paper “Methane emissions from terrestrial plants under aerobic conditions” by Keppler et al,

” If our measurements are typical for short-lived biomass and scaled on a global basis, we estimate a methane source strength of 62–236 Tg yr-1 for living plants and 1–7 Tg yr-1 for plant litter (1 Tg = 1012 g). We suggest that this newly identified source may have important implications for the global methane budget and may call for a reconsideration of the role of natural methane sources in past climate change.”

“A recent study by the CERES Science Team has added to the uncertainty associated with the contributions of climate forcings to global warming by finding that for the period 2000-2004, their assessment of the shortwave albedo decreased by 0.0015 which corresponds to an extra 0.5 Watts per meter squared of radiative imbalance according to their assessment. (see)

“Deposition of BC aerosols over snow-covered areas can result in changes to the surface albedo (Chylek et al. 1983). Further reductions in albedo occur due to the enhanced melting that accompanies the heating of absorbing soot particles in snow. Chylek et al. (1983) estimate this enhancement to be up to a factor of ten in the rate of melting. Recent model results indicate radiative forcings of +0.3 W m−2 in the Northern Hemisphere associated with albedo effects of soot on snow and ice (Hansen and Nazarenko 2004).�? (see)

We can summarize these findings below:

i) “ozone was responsible for one-third to half of the observed warming trend in the Arctic during winter and spring”.

ii) “The new interpretations reveal methane emissions may account for a third of the climate warming from well-mixed greenhouse gases between the 1750s and today”.

iii) “for the period 2000-2004, their assessment of the shortwave albedo decreased by 0.0015 which corresponds to an extra 0.5 Watts per meter squared of radiative imbalance according to their assessment.”

iv) “Recent model results indicate radiative forcings of +0.3 W m−2 in the Northern Hemisphere associated with albedo effects of soot on snow and ice”

v) There are a variety of direct and indirect aerosol effects that cause global warming including the black carbon direct effect, the semidirect indirect effect, and the glaciation indirect effect, with the thermodynamic effect having an unknown influence (see).

If we use the IPCC estimate of the fraction of the radiative forcing change of the well-mixed greenhouse gases from the pre-industrial to the present (i.e. see), which is about 2.4 Watts per meter squared, we can use the estimates of the radiative forcing from the other human climate forcings that are listed above to compare with this value, and with the fraction of the well-mixed greenhouse gas forcing that is due to CO2.

It need to be emphasized that the IPCC figure of the radiative forcing of 1.4 Watts per meter squared due to CO2 is not the current radiative imbalance since, presumably, some of the imbalance earlier in the industrial period with respect to CO2 increases has been removed as the climate system warmed. Nonetheless, these values can be used to scale the relative contribution to global warming due to the radiative effect of CO2. Also, since the observed radiative imbalance based on the 2004 Willis et al assessment is significantly less than the change from preindustrial to the present, the effect of human climate forcings that cool the climate system are, of course, also occurring.

With respect to the finding listed above, methane has a value of 0.8 Watts per meter squared, the shortwave albedo change is 0.5 Watts per meter squared, and the albedo effect of soot is 0.3 Watts per meter squared (which, however, may not be independent of the “shortwave albedo change). Tropospheric ozone, the aerosol black carbon direct effect, the semidirect indirect effect, and the glaciation indirect effect also add Watts per meter squared.

By summing the 0.8 Watts per meter squared for methane and using the total of 2.4 Watts per meter squared of the well-mixed greenhouse gases from the IPCC Report, the radiative contribution of CO2 reduces to about 46% of this component of radiative forcing (1.1 Watts per meter squared). The 46% value, of course, assumes that none of the radiative forcing of CO2 emitted earlier in the industrial period has equilibrated, so that the 46% is actually a high number, but is used here to be conservative.

For all of the human-caused warming radiative forcings, which includes the 0.5 Watts per meter squared value for the shortwave albedo change, and estimating tropospheric ozone as 0.3 Watts per meter squared, the aerosol black carbon direct effect as 0.2 Watts per meter squared, the black carbon on snow and ice as 0.3 Watts per meter squared, the semidirect indirect effect as 0.1 Watt per meter squared, and the glaciation indirect effect as 0.1 Watt per meter squared (with the latter two forcings using a nominal value, since these forcings are very poorly known), the contribution due to CO2 will fall to about 28%.

This analysis also ignores solar influences on the heating in which a published paper concludes,

“We estimate that the sun contributed as much as 45–50% of the 1900–2000 global warming, and 25–35% of the 1980–2000 global warming. ‘ (see). Even the IPCC estimates that there has been a warming influence from the Sun in their radiative forcing summary figure of about 0.25 Watts per meter squared (see). Adding this 0.25 Watts per meter squared value reduces the percent contribution of CO2 to about 26.5%.

This calculation does not mean that there is not merit in reducing the human input of CO2 into the atmosphere, but it does mean that even in the context of global warming, it is only a fraction of the actual positive radiative forcings.

This specific weblog focuses on the specific subset of climate variability and change that is referred to as “global warming” However, the assessment of radiative forcing directly is not the most appropriate procedure to use to assess global climate system heat changes. As was discussed in 2003 in “Heat storage within the Earth system”, the ocean heat content change is the proper metric to monitor.

Moreover, the observed ocean heat content changes have been spatially complex as has been discussed on this weblog (see and see). As reported based on the paper in 2004 by Willis et al that has been discussed on Climate Science (e.g. see and see),

“Maps of yearly heat content anomaly show patterns of warming commensurate with ENSO variability in the tropics, but also show that a large part of the trend in global, oceanic heat content is caused by regional warming at midlatitudes in the Southern Hemisphere.�?

This heating is

“…centered on 40S is spread more uniformly over the water column and warms steadily throughout the entire time series…�?

The climate science that is presented in this weblog summarizes one of the reasons for the conclusion that,

‘Attempts to significantly influence regional and local-scale climate based on controlling CO2 emissions alone is an inadequate policy for this purpose. ” (see).

The primary focus on carbon dioxide inappropriately deemphasizes the first order importance of the other climate system heat system forcings (both cooling and warming forcings), as well as does not address the spatially complex, and incompletely understood actual pattern of global climate system heat changes.

With so much discussion of global warming and climate change, what are the most appropriate metrics with respect to these environmental issues?

As discussed in the 2005 National Research Council Report (see), and illustrated on page 24 of that report, the focus (actually the icon) of global warming and climate change has been the global average surface temperature. As has been discussed several times on this weblog (e.g. see and see), this is a particularly poor climate metric even for global warming.

However, more appropriately, we need to identify those climate variables which significantly affect social and/or environmental issues of importance. This connects directly to the vulnerability paradigm which has been emphasized on the Climate Science weblog (e.g. see).

As examples, for a farmer, the important climate measures include:

1. length of growing season for their particular crops
2. the availability of natural and/or irrigated water for their crops
3. daytime temperatures including extreme heat which affects crop maturation
4. nighttime temperatures including falling below cold thresholds which threatens plant crop morbidity and mortality.
5. soil moisture levels which are required for optimal crop growth.

Each of these climate metrics are intimately related to local surface temperatures.

We examined several aspects of local 20th century temperature trends, as related to the above climate metrics, in our papers:

Pielke Sr., R.A., T. Stohlgren, L. Schell, W. Parton, N. Doesken, K. Redmond, J. Moeny, T. McKee, and T.G.F. Kittel, 2002: Problems in evaluating regional and local trends in temperature: An example from eastern Colorado, USA. Int. J. Climatol., 22, 421-434.

Pielke Sr., R.A., T. Stohlgren, W. Parton, J. Moeny, N. Doesken, L. Schell, and K. Redmond, 2000: Spatial representativeness of temperature measurements from a single site. Bull. Amer. Meteor. Soc., 81, 826-830.

Thus, can the multi-decadal climate models provide skillful forecasts of the temperature information that is required for these climate metrics?

The CCSP Report has an illuminating response, relative to this question, to one of my comments in my Public Comment (see lines 13-16; page 145 from the Responses to the Public Comment) that I asked on regional prediction skill. In the CCSP Committee response on page 145, lines 24-26, it states

“Owing to natural internal variability, models cannot be expected to reproduce regional patterns of trend over a periods as short as 20 years from changes of radiative forcings alone.”

This statmement, instead of being buried within the 159 page CCSP Response to the Public Comments, should be highlighted as a major conclusion of the CCSP Report which, afterall, is entitled ” Temperature Trends in the Lower Atmosphere: Steps for Understanding and Reconciling Differences “.

If the models cannot even skillfully predict regional linear trends in surface and tropospheric temperatures, how can they be expected to predict the societally and environmentally important local scale climate metrics that are listed earlier in this weblog, and which the farmer needs? If linear trends cannot be predicted, than the models also cannot predict the sudden climate changes (the tipping points) that have been clearly articulated as being important (see).

Thus the answer to the question, “can the multi-decadal climate models provide skillful forecasts of the temperature information that is required for these local climate metrics, as even admitted by the CCSP Committee itself, is clearly NO, on time periods of at least 20 years!

The obvious next question is “on what time periods has there been evidence of regional model prediction skill in these climate metrics?” Addressing this question should have been one of foci of the CCSP Report. Unfortunately, a useful answer to this question was not provided.

The website RealClimate had a very informative set of questions from Tom Cole and answers from Gavin Schmidt . RealClimate provides a valuable service by providing a set of issues in this Q&A format that we can answer. I provide my perspective on the questions below, in order to add to this discussion.

1. What schemes are you using for solving the partial differential equations? Are they free of numerical errors?

No model is free of numerical errors.

Climate models require the accurate simulation of the ocean, atmosphere, land, and continental ice. Physical, chemical, and biological processes must be included. In the atmospheric and ocean components of these models only the pressure gradient force and advection are represented in terms of fundamental concepts. This part of the models is referred to as the “dynamic core.” All other processes in these models are parameterized (e.g., turbulence, cloud and precipitation, short- and long-wave radiative fluxes).
The dynamical core of the models has been represented with finite difference and spectral methods; the latter of which is typical for global models, while regional climate models generally have applied finite differencing. For spatial scales less then 4 grid increments (or its equivalence in a spectral model), there is always serious numerical error (either in terms of preservation of amplitude and/or phase). For finite difference models, this is discussed in detail in Chapter 10 “Methods of Solution�? of Pielke, R.A., Sr., 2002: Mesoscale meteorological modeling. 2nd Edition, Academic Press, San Diego, CA, 676 pp.

This inability of models to skillfully simulate the smallest features within the grid structure is why the term “resolution” should be reserved to refer to spatial scales of at least 4 grid increments in each direction. This limitation applies to both finite difference and spectral models (see Pielke, R.A., 1991: A recommended specific definition of “resolution”, Bull. Amer. Meteor. Soc., 12, 1914; Pielke Sr., R.A., 2001: Further comments on “The differentiation between grid spacing and resolution and their application to numerical modeling”. Bull. Amer. Meteor. Soc., 82, 699; and Laprise, R., 1992: The resolution of global spectral models. Bull. Amer. Meteor. Soc., 9, 1453-1454.

Parameterizations that are used in the models have been vertical (i.e., one-dimensional) column or box models, and always include adjustable, tunable coefficients and functions. They are engineering codes which are calibrated based on observations, sometime in conjunction with higher resolution models, usually from what are often referred to as “golden days.” Golden days are selected with ideal conditions in order to best fit the theoretical framework of the parameterization. Since parameterizations are applied in the climate models for situations in which the parameterizations were not calibrated, there certainly are errors but of an unknown magnitude.

A powerpoint presentation that overviewed these issues is available at (Pielke, R.A., Sr., 2004: The Limitations of Models and Observations. COMET COMAP Symposium 04-1 on Planetary Boundary Layer Processes, Boulder, Colorado, June 21-25, 2004.).

2. Have you made tests to determine if the model results depend on resolution? In other words, have you increased the detail sufficiently so that the results are no longer dependent upon the size of an individual grid box?

Model results are always dependent on the grid increments used. It is unreasonable to expect the one-dimensional column and box parameterizations to accurately represent real-world three-dimensional features that are spatially smaller than can be resolved by the model grid increments.

That resolution matters is shown quantitatively in the paper Castro, C.L., R.A. Pielke Sr., and G. Leoncini, 2005: Dynamical downscaling: Assessment of value restored and added using the Regional Atmospheric Modeling System (RAMS). J. Geophys. Res. - Atmospheres, 110, No. D5, D05108, doi:10.1029/2004JD004721. In that paper, Table 5 presents a suite of regional climate experiments for horizontal grid intervals with 50 km, 100 km and 200 km spacing. The degradation in model skill as the horizontal increment is increased is shown.

Another issue, that the global climate models have not adequately addressed, is how well do they perform when initialized in a numerical weather prediction mode in terms of such atmospheric features as extratropical and tropical cyclogenesis? This test is a necessary condition of the accuracy of both the dynamics and parameterizations within the model. Since some global climate models have a parentage from numerical weather prediction code, this would be straightforward for them to evaluate with the code as adapted for long-term climate simulations. Clearly, a global model that is superior to others when it is run as a weather forecast, with observed initial conditions, will be a superior climate model as this means its dynamics and parameterizations are more accurate. Such comparison experiments starting from initial conditions have not been performed and documented in the literature to my knowledge. This approach would be an extension of the Atmospheric Model Intercomparison Project (AMIP) comparisons (i.e., as discussed, for example in Research Activities in Atmospheric and Oceanic Modeling, J. Cote, Ed.).

3. What are the dominant external forcing functions?

Figure 1-2 in the 2005 National Academy report defines natural forcings as from the Sun, due to the Earth’s orbital characteristics, and from volcanoes. Natural as defined here is meant to include forcings which reside external from the climate system. With this definition, the human-climate forcings are not external forcings.

4. What are the sources of intrinsic variability?

We do not know all of the reasons for the intrinsic (internal) variability of the climate system. Gavin has clearly identified some of them. However, the paper by Rial, J., R.A. Pielke Sr., M. Beniston, M. Claussen, J. Canadell, P. Cox, H. Held, N. de Noblet-Ducoudre, R. Prinn, J. Reynolds, and J.D. Salas, 2004: Nonlinearities, feedbacks and critical thresholds within the Earth’s climate system. Climatic Change, 65, 11-38, illustrates observed examples on a variety of time scale of sudden and rapid transitions of climate which we do not adequately understand. One major conclusion, however, is that intrinsic, complex variability results from interfacial nonlinear interactions between the components.

5. How do errors in estimating the forcing functions, or in simulating the internal variability impact the results?

I agree with Gavin that this is a good question. However, until we include all of the first-order climate forcings and feedbacks, as well as successfully model sudden climate transitions, we have large remaining errors of an unknown magnitude. We also have to show prediction skill in the quasi-linear global and regional long term trends of important climate metrics (regional precipitation, regional layer-averaged tropospheric temperatures, etc). Whether or not we agree the models have shown skill in reproducing global temperature averages or not, they certainly have not demonstrated regional skill for the spectrum of important climate metrics.

These first-order climate forcings are identified in the National Research Council report “Radiative Forcing of Climate Change: Expanding the Concept and Addressing Uncertainties”, while the first-order climate feedbacks are identified in “Understanding Climate Change Feedbacks”. Sudden climate transitions are discussed in the National Research Council report “Abrupt Climate Change: Inevitable Surprises” .

6. The minimum amount of observed data that you have to reproduce in order to gain some confidence in your model is that you have to reproduce periods of time when temperatures are increasing and when they are decreasing. Have you queried the model as to what the dominant mechanism(s) is/are that caused the cooling? If so, is/are the mechanism(s) plausible? Can they be verified independently?

Gavin stated “This isn’t much of a test. The models are pretty stable in the absence of forcing changes (although there is some centennial variability as noted above, related mostly to ocean circulation/sea ice interactions).”

As illustrated in the National Research Council reports and in the Rial et al. paper, however, the observations show that the climate system is not “pretty stable” even without clear changes in the external forcing, If the models are unable to skillfully simulate and explain abrupt regional climate change, they are of limited use in describing our real risk to human-caused and natural climate change and variability. We need to move beyond “linear climate change” thinking.

Also, we need to move beyond global mean surface temperature (and even global mean tropospheric temperature) as the primary climate change metrics. This was a clear conclusion of the 2005 National Research Council report . We need to focus on climate metrics such as drought, growing season and floods, for example, which are climate effects that directly impact society and the environment.

7. Have you tested the model against simplified analytical solutions? Are you able to accurately reproduce analytical results?

Gavin’s answer is correct. We need to use observations to test the models. This is one reason that there is considerable interest in using global and regional climate models to simulate prehistoric and historic climates.

8. How do you address the issue that models cannot be used to predict the future? In other words, models can only predict what might happen under a given set of conditions, not what will happen in the future.

The IPCC and US National Assessment results have been interpreted as predictions. To use the word ‘projection’ to indicate they are different than ‘prediction’ is a nuance that is lost by almost everyone. Indeed the Webster’s New World Dictionary (1988 edition) has one definition of a projection as “a prediction or advance estimate based on known data or observations; extrapolations.” A projection is a prediction! See also my July 15 and 22, 2005 weblogs entitled What Are Climate Models? What Do They Do? and Are Multi-decadal Climate Forecasts Skillful? , and my 2002 Climatic Change essay Overlooked issues in the U.S. National Climate and IPCC assessments on this subject.

We do have a serious problem in climate science in that the same individuals who perform the research are completing the climate assessment reports. This is equivalent to the authors of a research paper, and their close collaborators, review their submitted paper! When I served as Chief Editor of the Monthly Weather Review and Co-Chief Editor of the Journal of Atmospheric Science, this type of procedure was never was permitted. It should certainly not be allowed for the CCSP and IPCC reports, and, to the extent it is, those reports should be interpreted as advocacy documents and not a balanced review of climate science (see, for example my October 4 2005 weblog entitled “Overlooked Issues in Prior IPCC Reports and the Current IPCC Report Process: Is There a Change From the Past?.”

10. I have been working on the same code for over 27 years, and I can guarantee that it is not bug free. A debuggers job is never done. How long has your code been in development?

The more serious error in the models is their incomplete representation of the climate system including the accurate representation of all first-order climate forcings and feedbacks. We also need to know the sensitivity of the model results due to the uncertainly in the parameterizations and from the spatial resolution used. Coding bugs, while as anyone who has written code realizes, never completely disappear as the model is applied to new situations, is not a major problem with climate model simulations that I am aware of.

As my final comment, I want to add to Gavin’s closing remarks, reproduced below

“On a final note, an implicit background to these kinds of questions is often the perception that scientific concern about global warming is wholly based on these (imperfect) models. This is not the case. Theoretical physics and observed data provide plenty of evidence for the effect of greenhouse gases on climate. The models are used to fill out the details and to make robust quantiative projections, but they are not fundamental to the case for anthropogenic warming. They are great tools for looking at these problems though.”

Models are a powerful tool to better understand the climate system and to assess the sensitivity of the climate system to human and natural climate forcings. They have shown us that the radiative effect of the of addition of greenhouse gases is a first-order climate forcing that alters our climate.

However, where I and others disagree with Gavin is the statement that “The models are used to fill out the details and to make robust quantitative projections…”. What “details” and what demonstration of “robust quantitative projections�??This blanket statement needs to be clarified. Even Mike MacCracken and colleagues for example, have published a paper in Nature in 2004 entitled “Reliable regional climate model not yet on horizon.” The overselling of regional and global models as skillful (robust) projections, unfortunately, rather than as sensitivity simulations, adds to the existing politicalization of climate science and provides justifiable criticism of the assessment reports that are published.