I have posted in the past how the development of multi-decadal regional climate projections (predictions) to give to policymakers and the impact communities is a huge waste of time and resources; e.g. see
The Huge Waste Of Research Money In Providing Multi-Decadal Climate Projections For The New IPCC Report
Today, I want to discuss this issue in relation to the Working Group I Contribution to the IPCC Fifth Assessment Report Climate Change 2013: The Physical Science Basis
Projections are presented in Annex 1:
This is titled
Annex I: Atlas of Global and Regional Climate Projections
The foundation of this Atlas is based on the information provided in Chapter 11 of the IPCC WG1 report titled
“Near-term Climate Change: Projections and Predictability”
Multi-decadal regional climate projections, of course, can obviously not be any better than shorter term (i.e. “near-term”) projections (e.g. decadal) since decade time periods make up the longer period! The level of skill achieved for decadal time scales, must be the upper limit on what is obtainable for longer time periods.
As written in Chapter 11
Climate scientists distinguish between decadal predictions and decadal projections. Projections exploit only the predictive capacity arising from external forcing.
Projections then are simply model sensitivity simulations. By ignoring internal climate dynamics their presentation to the impacts communities as scenarios is a gross overstatement of what they really provide. They are only useful as improving our understanding of a subset of climate processes. To present results from them in the IPCC report without emphasizing this important limitation is not an honest communication.
The issue of how the climate model results are presented should bother everyone, regardless of one’s view on the importance of greenhouse gases in the atmosphere.
Chapter 11, fortunately, in contrast to Annex 1, is an informative chapter in the 2013 IPCC WG1 report that provides a scientific summary regarding predictability although their discussion on the uncertainties of the “external climate forcings” and skill is incomplete (e.g. see http://pielkeclimatesci.files.wordpress.com/2009/12/r-354.pdf ).
The chapter focus is described this way
This chapter describes current scientific expectations for ‘near-term’ climate. Here ‘near term’ refers to the period from the present to mid-century, during which the climate response to different emissions scenarios is generally similar. Greatest emphasis in this chapter is given to the period 2016–2035, though some information on projected changes before and after this period (up to mid-century) is also assessed.
Skilful multi-annual to decadal climate predictions (in the technical sense of ‘skilful’ as outlined in 18.104.22.168 and FAQ 11.1) are being produced although technical challenges remain that need to be overcome in order to improve skill.
Some important extracts from the chapter are [highlight added]
Near-term prediction systems have significant skill for temperature over large regions (Figure 11.4), especially over the oceans (Smith et al., 2010; Doblas-Reyes et al., 2011; Kim et al., 2012; Matei et al., 2012b; van Oldenborgh et al., 2012; Hanlon et al., 2013). It has been shown that a large part of the skill corresponds to the correct representation of the long-term trend (high confidence) as the skill decreases substantially after an estimate of the long-term trend is removed from both the predictions and the observations (e.g., Corti et al., 2012; van Oldenborgh et al., 2012; MacLeod et al., 2013).
The skill in hindcasting precipitation over land (Figure 11.6) is much lower than the skill in hindcasting temperature over land.
The skill of extreme daily temperature and precipitation in multi-annual time scales has also been assessed (Eade et al., 2012; Hanlon et al., 2013). There is little improvement in skill with the initialization beyond the first year, suggesting that skill then arises largely from the varying external forcing. The skill for extremes is generally similar to, but slightly lower than, that for the mean.
As part of Chapter 11, there is a section on Frequently Asked Questions. I have extracted excerpts from the FAQ 11.1 which is titled
If You Cannot Predict the Weather Next Month, How Can You Predict Climate for the Coming Decade?
Excerpts read highlighted text.
“Climate scientists do not attempt or claim to predict the detailed future evolution of the weather over coming seasons, years or decades.”Meteorological services and other agencies … have developed seasonal-to-interannual prediction systems that enable them to routinely predict seasonal climate anomalies with demonstrable predictive skill. The skill varies markedly from place to place and variable to variable. Skill tends to diminish the further the prediction delves into the future and in some locations there is no skill at all. ‘Skill’ is used here in its technical sense: it is a measure of how much greater the accuracy of a prediction is, compared with the accuracy of some typically simple prediction method like assuming that recent anomalies will persist during the period being predicted.Weather, seasonal-to-interannual and decadal prediction systems are similar in many ways (e.g., they all incorporate the same mathematical equations for the atmosphere, they all need to specify initial conditions to kick-start predictions, and they are all subject to limits on forecast accuracy imposed by the butterfly effect). However, decadal prediction, unlike weather and seasonal-to-interannual prediction, is still in its infancy. Decadal prediction systems nevertheless exhibit a degree of skill in hindcasting near-surface temperature over much of the globe out to at least nine years. A ‘hindcast’ is a prediction of a past event in which only observations prior to the event are fed into the prediction system used to make the prediction. The bulk of this skill is thought to arise from external forcing. ‘External forcing’ is a term used by climate scientists to refer to a forcing agent outside the climate system causing a change in the climate system. This includes increases in the concentration of long-lived greenhouse gases.Theory indicates that skill in predicting decadal precipitation should be less than the skill in predicting decadal surface temperature, and hindcast performance is consistent with this expectation.Finally, note that decadal prediction systems are designed to exploit both externally forced and internally generated sources of predictability. Climate scientists distinguish between decadal predictions and decadal projections. Projections exploit only the predictive capacity arising from external forcing. While previous IPCC Assessment Reports focussed exclusively on projections, this report also assesses decadal prediction research and its scientific basis.
What is remarkable about this Chapter is that they now recognize that at least out to a decade skillful predictions are very difficult. Only the skill in hindcasting near-surface temperature over much of the globe out to at least nine years has been emphasized. Skillful multi-decadal projections must be even more challenging.
Yet, Annex 1 provides detailed regional projections decades out into the future. It is
Annex I: Atlas of Global and Regional Climate Projections
I have excerpted text from this Annex that explains what is provided (i.e. detailed regional multi decadal climate projections)
Annex I: Atlas of Global and Regional Climate Projections is an integral part of the Working Group I contribution to the IPCC Fifth Assessment Report, Climate Change 2013: The Physical Science Basis. It will provide comprehensive information on a selected range of variables (e.g., temperature and precipitation) for a few selected time horizons (e.g., 2020, 2050, and 2100) for all regions and, to the extent possible, for the four basic RCP scenarios.
However, there is a fundamental flaw in creating Annex 1, and, thus, any papers and studies on future climate impacts that result from it. Despite the widespread use of these model results, it is really a fundamentally flawed activity.
For this approach to be a robust approach to use for impact studies, these model results (when tested in hindcast) must show skill in not only replicating current climate (which is tested by comparison with reanalyses in which the climate model is NOT forced by the lateral boundary and nudging from the reanalyses), but must show skill at predicting CHANGES in regional climate statistics. This later requirement is a requirement to accept the models as robust projection (prediction) tools.
Necessary and Sufficient Tests of Model Prediction (Projection) Skill
· The ability of the model to skillfully reproduce the regional climate statistics from the climate model (from the GCM or downscaled by a higher resolution regional model) is a NECESSARY first condition.
· The REQUIRED condition is that they must show, in hindcast runs, skill at predicting CHANGES in regional climate statistics.
There is a common mistake is to assume that one can use reanalyses to assess model prediction skill for the future. However, as discussed, for example, in the paper
Pielke Sr., R.A., and R.L. Wilby, 2012: Regional climate downscaling – what’s the point? Eos Forum, 93, No. 5, 52-53, doi:10.1029/2012EO050008
using reanalyses to drive a model places a real world constraint on the results which does not exist when the multi-decadal climate models are run for the future decades (and indeed, lateral boundary conditions and nudging from the reanalyses must not be used in true hindcast tests of model skill). This issue is discussed in the paper
Pielke Sr., R.A. 2013: Comments on “The North American Regional Climate Change Assessment Program: Overview of Phase I Results.” Bull. Amer. Meteor. Soc., 94, 1075-1077, doi: 10.1175/BAMS-D-12-00205.1.
As discussed above, unless the global climate model (dynamically and/or statistically downscaled) can be shown to skillfully predict current climate on the regional scales [when run over multi-decadal time scales in a hindcast mode, it cannot be accepted as a faithful representation of the real world climate.
Examples of IPCC Model Shortcomings
Multi-decadal global model prediction, in hindcast runs, however, have major shortcoming even with respect to current climate! Peer reviewed examples of these shortcomings include; as summarized in the Preface to
Pielke Sr, R.A., Editor in Chief., 2013: Climate Vulnerability, Understanding and Addressing Threats to Essential Resources, 1st Edition. J. Adegoke, F. Hossain, G. Kallos, D. Niyoki, T. Seastedt, K. Suding, C. Wright, Eds., Academic Press, 1570 pp. [http://pielkeclimatesci.files.wordpress.com/2013/05/b-18preface.pdf]
Taylor et al, 2012: Afternoon rain more likely over drier soils. Nature. doi:10.1038/nature11377. Received 19 March 2012 Accepted 29 June 2012 Published online 12 September 2012
“…the erroneous sensitivity of convection schemes demonstrated here is likely to contribute to a tendency for large-scale models to `lock-in’ dry conditions, extending droughts unrealistically, and potentially exaggerating the role of soil moisture feedbacks in the climate system.”
Driscoll, S., A. Bozzo, L. J. Gray, A. Robock, and G. Stenchikov (2012), Coupled Model Intercomparison Project 5 (CMIP5) simulations of climate following volcanic eruptions, J. Geophys. Res., 117, D17105, doi:10.1029/2012JD017607. published 6 September 2012.
“The study confirms previous similar evaluations and raises concern for the ability of current climate models to simulate the response of a major mode of global circulation variability to external forcings.”
Fyfe, J. C., W. J. Merryfield, V. Kharin, G. J. Boer, W.-S. Lee, and K. von Salzen (2011), Skillful predictions of decadal trends in global mean surface temperature, Geophys. Res. Lett.,38, L22801, doi:10.1029/2011GL049508
”….for longer term decadal hindcasts a linear trend correction may be required if the model does not reproduce long-term trends. For this reason, we correct for systematic long-term trend biases.”
Xu, Zhongfeng and Zong-Liang Yang, 2012: An improved dynamical downscaling method with GCM bias corrections and its validation with 30 years of climate simulations. Journal of Climate 2012 doi: http://dx.doi.org/10.1175/JCLI-D-12-00005.1
”…the traditional dynamic downscaling (TDD) [i.e. without tuning) overestimates precipitation by 0.5-1.5 mm d-1.....The 2-year return level of summer daily maximum temperature simulated by the TDD is underestimated by 2-6°C over the central United States-Canada region".
Anagnostopoulos, G. G., Koutsoyiannis, D., Christofides, A., Efstratiadis, A. & Mamassis, N. (2010) A comparison of local and aggregated climate model outputs with observed data. Hydrol. Sci. J. 55(7), 1094–1110
".... local projections do not correlate well with observed measurements. Furthermore, we found that the correlation at a large spatial scale, i.e. the contiguous USA, is worse than at the local scale."
Stephens, G. L., T. L’Ecuyer, R. Forbes, A. Gettlemen, J.‐C. Golaz, A. Bodas‐Salcedo, K. Suzuki, P. Gabriel, and J. Haynes (2010), Dreary state of precipitation in global models, J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.
"...models produce precipitation approximately twice as often as that observed and make rainfall far too lightly.....The differences in the character of model precipitation are systemic and have a number of important implications for modeling the coupled Earth system .......little skill in precipitation [is] calculated at individual grid points, and thus applications involving downscaling of grid point precipitation to yet even finer‐scale resolution has little foundation and relevance to the real Earth system.”
Sun, Z., J. Liu, X. Zeng, and H. Liang (2012), Parameterization of instantaneous global horizontal irradiance at the surface. Part II: Cloudy-sky component, J. Geophys. Res., doi:10.1029/2012JD017557, in press.
“Radiation calculations in global numerical weather prediction (NWP) and climate models are usually performed in 3-hourly time intervals in order to reduce the computational cost. This treatment can lead to an incorrect Global Horizontal Irradiance (GHI) at the Earth’s surface, which could be one of the error sources in modelled convection and precipitation. …… An important application of the scheme is in global climate models….It is found that these errors are very large, exceeding 800 W m-2 at many non-radiation time steps due to ignoring the effects of clouds….”
Ronald van Haren, Geert Jan van Oldenborgh, Geert Lenderink, Matthew Collins and Wilco Hazeleger, 2012: SST and circulation trend biases cause an underestimation of European precipitation trends Climate Dynamics 2012, DOI: 10.1007/s00382-012-1401-5
“To conclude, modeled atmospheric circulation and SST trends over the past century are significantly different from the observed ones. These mismatches are responsible for a large part of the misrepresentation of precipitation trends in climate models. The causes of the large trends in atmospheric circulation and summer SST are not known.”
I could go on with more examples. However, it is clear that the climate models used in the manuscript under review are not robust tools to use to predict climate conditions in the future.
Annex 1 of the 2013 IPCC WG1 report, therefore, is fundamentally flawed as it is based on multi-decadal climate model results which have not shown skill at faithfully replicating most of the basic climate dynamics, such as major atmospheric circulation features, even in the current climate. They have also shown no skill at predicting the CHANGES of regional climate statistics to the accuracy required for impact studies.
Views by Others
Now, in closing, below I have extracted text from separate e-mails of two very major well known players in the climate area. Both accept that CO2 is the dominant climate forcing and man is responsible and we need urgent action. These quotes are in e-mails that I have. They show that despite other topics in which we disagree, they presumably would agree with me on the gross inadequacies of Annex 1 in the IPCC WG1 report.
The relevant part of the first e-mail reads
“It is also worth pointing out that neither initialised decadal predictions nor RCMs are the entirety of what can be said about regional climate change in the next few decades – and in fact, it is arguable whether either add anything very much. ;-)”
The second e-mail reads
“The climate effects are largely warming, I cannot say today’s climate models can tell us much more with any certainty. I will add that there is probably poleward and continental intensification of the warming. One further feature that appears to be robust is the movement of the storm belts in both hemispheres polewards. This has implications for the general circulation and in particular the climatology of precipitation intensity and variability (i.e., drought and flood). This poleward shift is seen in the data. There is some model evidence that over the next century ozone recovery could cancel come of this poleward shift in the Southern Hemisphere, but probably not in the NH. I think this is about as far I as we can go in forecasting climate over the next 50-100 years. Of course, sea level follows naturally from thermal expansion of the water as well as land ice melting. I think there is very little information (above noise) beyond the above in global climate models at regional level, even including downscaling.”
Since, these individuals have been silent in discussing the issue of the value of multi-decadal model regional climate predictions, I feel compelled to communicate that my flagging the failure of this approach for the impacts and policymakers communities is shared by even some in the IPCC community.
Recommendation for Responding to this IPCC Deficiency
My recommendation is that when you hear or read of climate projections on multi-decadal time periods, ask them:
What is the quantitative skill of the models used in predicting their projected CHANGES? In other words, what is the predictive skill with respect to the climate metrics that are of importance to a particular impact?
If they cannot present quantitative evidence of such skill they are inappropriately presenting their studies. Annex 1 of the 2013 IPCC WG1, therefore, still needs an honest demonstration of the skill (if any) of their projections as part of a complete assessment of the state of climate science.