Comment On Judy Curry’s Post “Scenarios: 2010-2030. Part I”

Judy Curry at Climate Etc has another thoughtful and informative post titled

Scenarios: 2010-2030. Part I

In her post, she discusses (for this time scale)

“It is the combination of … natural variability and forced anthropogenic climate change that is of particular interest.   Natural variability dominates regional climate change in many locations.  Further, decision makers need to know the extent to which climate is varying because of natural variability, and hence expected to reverse at some point, or whether the climate is changing as the result of irreversible anthropogenic forcing.

So how should we approach this problem?  Is there predictability in the climate system on these timescales?  If so, how can this predictability be realized and converted into useful predictions?”

and writes

“The targets of interest on the timescale of the next two decades are

  • the evolution of the global average temperature anomaly, for understanding the relative roles of anthropogenic versus natural climate variability/change
  • the evolution of regional climate variability to support regional decision making: average temperature and precipitation; extreme events (heat/cold waves, floods, droughts, hurricanes, wildfires, etc).”

I would write these different in terms of what is needed by policymakers:

  • There are 5 broad areas that we can use to define the need for vulnerability assessments on all time scales : water, food, energy, health and ecosystem function. Each area has societally critical resources. There is a need to determine the vulnerability of these resources to the major threats to these resources from climate (e.g. for the next 20 years), but also from other social and environmental issues. After these threats are identified for each resource, then the relative risk from natural- and human-caused climate change (estimated from the global climate model multi-decadal projections, but also the historical, paleo-record and worst case sequences of events) can be compared with other risks in order to adopt the optimal mitigation/adaptation strategy (e.g. see)
  • Do the global climate multi-decadal projections results skillfully predict observationally documented CHANGES in the statistics (probabilities) of the different major circulation patterns, as well as their behaviour in coming years, and can these predictions be separated into a “natural” and a “human-caused’ component?
  • The global average surface temperature is an inadequate diagnostic for global warming and cooling. The skill of the global climate multi-decadal projections to predict the change in ocean heat content in Joules over the coming decade is a much more robust metric to assess global climate system heat changes (e.g see).

Judy further reports

Hoerling et al. (2010, submitted) has conducted an interesting set up experiments that makes predictions for the North American climate for 2011-2020:

North American mean surface air temperature and precipitation are predicted for the upcoming 2011-2020 decade. Multiple climate models forced by various plausible scenarios for the 2011-2020 change in ocean surface boundary conditions are first employed in order to estimate the forced response, and its uncertainty, to expected changes in anthropogenic forcing. A full probabilistic decadal forecast is then generated by commingling the statistics of the forced response with those arising from internal decadal sea surface temperature (SST) and sea ice variability. The latter are estimated from a multi-model suite of 20th Century atmospheric climate simulations driven by the observed time history of SST and sea ice variations.

The prediction is characterized by surface warming over the entire continent and precipitation decreases (increases) over the contiguous United States (Canada) relative to 1971-2000 conditions. The signs of these signals are robust across the scenarios and the models employed, though their amplitudes are not. An assessment of the sources of forecast uncertainty reveals comparable sensitivity to the various scenarios of forced SST change, model dependency, internal atmospheric noise, and internal decadal SST variability. Taking these sources of forecast uncertainty into account, predictions for the 2011-2020 decade indicate a 94% and 98% probability for warmer than normal conditions over the U.S. and Canada, respectively, a 99% probability of wet conditions over Canada, and a 75% probability of dry conditions over the U.S.”

These  forecasts are on a time period where we can actually compare with observations (2011 to 2020). This is worthwhile as these results are actually testable.

I agree with Judy where she writes

“The key challenge of multi-decadal climate forecasting is prediction of the change points (transitions) of the major ocean oscillations.   Again, based on my experience with probabilistic seasonal forecasting, the only way I see to do this potentially with any skill is to select the models that do a relatively good job at simulating the key features in hindcast mode, and then select the ensemble members from these models that compare best with observations for the first year or two of the simulation.  The rationale for such a selection is that ensemble members that get off to a good start are more likely to be on a good trajectory going forward.   I look forward to getting my hands on the CMIP5 simulations.”

I have discussed the value of these multi-decadal global model predictions in a comment on an article by Hurrell et al (2009)


Pielke Sr., R.A., 2010: Comment on “ A Unified Modeling Approach to Climate System Prediction“, Bull. Amer. Meteor. Soc., DOI:10.1175/2010BAMS2975., in press [it has taken BAMS well over a year to publish].

 My conclusion reads [and this applies to the Hoerling et al study that Judy refers to]

“Thus, although I commend the authors for starting to adopt 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 policy makers. A significant fraction of the funds they are seeking for prediction could more effectively be used if they were spent on assessing risk and ways to reduce the vulnerability of local/regional resources to climate variability and change and other environmental issues using the bottom-up, resources-based perspective discussed in Pielke and Bravo de Guenni (2004), Pielke (2004), and Pielke et al. (2009). This bottom-up focus is “of critical interest to society.”

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