Comments On A New Paper “A Unified Modeling Approach to Climate System Prediction” By Hurrell Et Al 2009

There is a new paper that will appear soon [thanks to Marcel Crok for alerting us to it!]. It is

 James Hurrell, Gerald A. Meehl, David Bader, Thomas L. Delworth, Ben Kirtman, and Bruce Wielicki 2009: A Unified Modeling Approach to Climate System Prediction. Bulletin of the American Meteorological Society, in press.

The abstract reads

“Demand for more accurate predictions of regional climate necessitates a unified modeling approach explicitly recognizing that many processes are common to predictions across time scales. Applying and testing models with this approach has many benefits.”

In the following, I comment on excerpts from the text:

Excerpt:

“There is a new perspective of a continuum of prediction problems, with a blurring of the distinction between short-term predictions and long-term climate projections. At the heart of this new perspective is the realization that all climate system predictions, regardless of time scale, share common processes and mechanisms; moreover, interactions across time and space scales are fundamental to the climate system itself. Further, just as seasonal to interannual predictions start from an estimate of the state of the climate system, there is a growing realization that decadal and longer term climate predictions could be initialized with estimates of the current observed state of the atmosphere, oceans, cryosphere, and land surface……The global coupled atmosphere-ocean-land-cryosphere system exhibits a wide range of physical and dynamical phenomena with associated physical, biological and chemical feedbacks that collectively result in a continuum of temporal and spatial variability. The traditional boundaries between weather and climate are, therefore, somewhat artificial…….The central unifying theme is that all climate system predictions, regardless of time scale, share processes and mechanisms that consequently could benefit from initialization of coupled general circulation models with  best estimates of the observed state of the climate”

Comment By Roger A. Pielke Sr.:

This is not a new perspective. This viewpoint was reported on in detail in the report

National Research Council, 2005: Radiative forcing of climate change: Expanding the concept and addressing uncertainties. Committee on Radiative Forcing Effects on Climate Change, Climate Research Committee, Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies, The National Academies Press, Washington, D.C., 208 pp.

I also discussed this subject over a decade ago in

Pielke, R.A., 1998: Climate prediction as an initial value problem. Bull. Amer. Meteor. Soc., 79, 2743-2746

The authors of the paper should have completed a more thorough review of the past literature. At least they finally recognize that climate is an initial value problem!

Excerpt:

Fundamental barriers to advancing weather and climate prediction on time scales from days to years, as well as long-standing systematic errors in weather and climate models, are partly attributable to our limited understanding and capability to simulate the complex, multi-scale interactions intrinsic to atmospheric, oceanic and cryospheric fluid motions.

Comment By Roger A. Pielke Sr.:

If they are “partly attributable to our limited understanding”, what are our other barriers? The fundamental barrier is our limited understanding as to how the real world climate system actually works. The examples presented in the Hurrell et al paper actually show how difficult this subject is.

Excerpt:

For climate predictions, the initial state of the atmosphere is less critical, and states separated by a day or so can be substituted. However, the initial states of other climate system components become vital. For predictions of a season to a year or so, the SSTs, sea ice extent and upper ocean heat content, soil moisture, snow cover, and state of surface vegetation over land are all important……..A good rule of thumb for prediction is that an upper bound on predictability corresponds approximately to one lifecycle of the phenomenon being considered. Hence one could hope to predict a single convective element, cyclone wave, MJO cycle, ENSO warm event, or fluctuation of the Atlantic MOC over its lifecycle, but not the second generation event. This rule of thumb is consistent with the climate system being a chaotic dynamical system with limited predictability. Additional predictability, however, could arise from the slowly evolving components of the climate system.

Comment By Roger A. Pielke Sr.:

I agree with their summary, except that slowly evolving components also have long term variability. When they use phrases such as “rule of thumb” and “one could hope”,this should alert the reader to very qualitative character of their view. This is another way to state that this is a hypothesis. Indeed, since we know that climate undergoes sudden and significant shifts (e.g. see, see and see), the concept of a “cycle” is an inaccurate way to explain how the actual climate system works. In fact, the authors themselves write “the climate system [is] a chaotic dynamical system with limited predictability.”  Slowly evolving components can also result in a nonlinear response within the climate system.

Excerpt:

Although deterministic atmospheric predictability is limited to approximately two weeks (e.g., Kleeman 2007), on longer time scales at least two types of predictions may be possible. The first is a prediction of the internal variability of the climate system based on an initialized state of the ocean, atmosphere, land and cryosphere system…….In addition to the potential sources of predictability from the initial values of the system, predictability may also be derived from past and future changes in radiative forcing.

Comment By Roger A. Pielke Sr.:

To the extent that the system is influenced by the internal variability of the climate system based on an initialized state of the ocean, atmosphere, land and cryosphere system”, there will also be a limit to the time period of skillful prediction. Also, what role does “past radiative forcing” play in improving predictability? The effect of the past forcing is already in the initial conditions!

Excerpt:

For decadal and longer time scales, the problem of quantifying prediction skill becomes even more difficult, and the metrics will likely involve how the forecasts are used in applications. Even if we could test long term climate models with all possible climate metrics proposed in the last decade of journal papers, we have no current method to prioritize or weight their impact in measuring uncertainty in predicting future climate change for temperature, precipitation, soil moisture and other variables of critical interest to society.

 Comment By Roger A. Pielke Sr.:

This is an amazing admission. If “quantifying  prediction skill becomes even more difficult” for decadal and longer time scales and that we have no current method to prioritize or weight their impact in measuring uncertainty in predicting future climate change for temperature, precipitation, soil moisture and other variables of critical interest to society”, how is the proposed modeling approach to satisfy the “[d]emand for more accurate predictions of regional climate” as written by the authors in their abstract?

Thus, while I commend the authors for adopting a framework of climate modeling as an initial value problem, they are at serious risk of overselling what they will be able to provide to policymakers. Some of the funds they are seeking for this effort could be more effectively used, if they were spent on assessing risk and reducing the vulnerability of local/regional resources to climate variability and change and other environmental issues. This is what is “of critical interest to society”.

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