CMIP5 Climate model predictions for the coming decades is an integral part of the upcoming IPCC assessment. The CMIP5 - Coupled Model Intercomparison Project Phase 5 is intended to
“to promote a new set of coordinated climate model experiments. These experiments comprise the fifth phase of the Coupled Model Intercomparison Project (CMIP5). CMIP5 will notably provide a multi-model context for 1) assessing the mechanisms responsible for model differences in poorly understood feedbacks associated with the carbon cycle and with clouds, 2) examining climate “predictability” and exploring the ability of models to predict climate on decadal time scales, and, more generally, 3) determining why similarly forced models produce a range of responses.”
They report that
CMIP5 promotes a standard set of model simulations in order to:
- evaluate how realistic the models are in simulating the recent past,
- provide projections of future climate change on two time scales, near term (out to about 2035) and long term (out to 2100 and beyond), and
- understand some of the factors responsible for differences in model projections, including quantifying some key feedbacks such as those involving clouds and the carbon cycle
My post today is to summarize the lack of scientific value in those model predictions with respect to “evaluate how realistic the models are in simulating the recent past” and, thus their use to project (predict) “future climate change on two time scales, near term (out to about 2035) and long term (out to 2100 and beyond.” My post brings together information from several recent posts.
The first requirement of the CMIP5 runs, before they should even be spending time and money on projections, is that they must skillfully (and shown with quantitative analyses) to
- replicate the statistics of the current climate,
- replicate the changes in climate statistics over this time period.
However, peer-reviewed studies that have quantitatively examined this issue using hindcast runs show large problems even with respect to current model statistics, much less their change over time.
Examples of these studies include
1. 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
who concluded that
”….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.”
2. 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
who find that without tuning from real world observations, the model predictions are in significant error. For example, they found that
”…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."
3. van Oldenborgh, G.J., F.J. Doblas-Reyes, B. Wouters, W. Hazeleger (2012): Decadal prediction skill in a multi-model ensemble. Clim.Dyn. doi:10.1007/s00382-012-1313-4
who report quite limited predictive skill in two regions of the oceans on the decadal time period, but no regional skill elsewhere, when they conclude that
"A 4-model 12-member ensemble of 10-yr hindcasts has been analysed for skill in SST, 2m temperature and precipitation. The main source of skill in temperature is the trend, which is primarily forced by greenhouse gases and aerosols. This trend contributes almost everywhere to the skill. Variation in the global mean temperature around the trend do not have any skill beyond the first year. However, regionally there appears to be skill beyond the trend in the two areas of well-known low-frequency variability: SST in parts of the North Atlantic and Pacific Oceans is predicted better than persistence. A comparison with the CMIP3 ensemble shows that the skill in the northern North Atlantic and eastern Pacific is most likely due to the initialisation, whereas the skill in the subtropical North Atlantic and western North Pacific are probably due to the forcing."
4. 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
who report that
".... 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."
5. 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.”
6. 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.
who report that
“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. The radiation sampling error due to infrequent radiation calculations is investigated using the this scheme and ARM observations. 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….”
7. 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
who report that
“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.”
Even the most basic of climate model predictions that global average water vapor is increasing (and thus would amplify the radiative warming from added CO2) is in question; see
Vonder Haar, T. H., J. Bytheway, and J. M. Forsythe (2012), Weather and climate analyses using improved global water vapor observations, Geophys. Res. Lett.,doi:10.1029/2012GL052094, in press.
There is an important summary of the limitations in multi-decadal regional climate predictions in
Kundzewicz, Z. W., and E.Z. Stakhiv (2010) Are climate models “ready for prime time” in water resources managementapplications, or is more research needed? Editorial. Hydrol. Sci. J. 55(7), 1085–1089.
who conclude that
“Simply put, the current suite of climate models were not developed to provide the level of accuracy required for adaptation-type analysis.”
These studies, and I am certain more will follow, show that the multi-decadal climate models are not even skillfully simulating current climate statistics, as are needed by the impacts communities, much less CHANGES in climate statistics. At some point, this waste of money to make regional climate predictions decades from now is going to be widely recognized.