Not 2 Green | CLIMATE MODEL REVIEW
Since we don’t know future atmospheric CO2, volcanic eruptions, and whatever “ other climate drivers ” should be put into the IPCC models, why are we using these models to set policy ?
Chapter 1 Framing, Context, and Methods
1.5.4 Modelling Techniques, Comparisons and Performance Assessments
Numerical models, however complex, cannot be a perfect representation of the real world. Results from climate modelling simulations constitute a key line of evidence for the present Report, which requires considering the limitations of each model simulation. Changes to a model that enhance its fitness for one purpose can sometimes decrease its fitness for others, by upsetting a pre-existing balance of approximations
184.108.40.206 Model Tuning and Adjustment
Whether tuning should be performed to facilitate accurate simulation of long-term trends such as changes in global mean temperature over the historical era, or rather be performed for each process independently such that all collective behaviour is emergent, is an open question ( Schmidt et al., 2017; Burrows et al., 2018 ).
220.127.116.11 Ensemble Modelling Techniques
Such ensembles employ a single GCM or ESM in a fixed configuration, but starting from a variety of different initial states. In some experiments, these initial states only differ slightly. As the climate system is chaotic, such tiny changes in initial conditions lead to different evolutions for the individual realizations of the system as a whole.
Overall, we assess that increases in computing power and the broader availability of larger and more varied ensembles of model simulations have contributed to better estimations of uncertainty in projections of future change ( high confidence ) . Note, however, that despite their widespread use in climate science today, the cost of the ensemble approach in human and computational resources, and the challenges associated with the interpretation of multi-model ensembles, has been questioned ( Palmer and Stevens, 2019; Touzé-Peiffer et al., 2020 ).
The AR5 quantified uncertainty in CMIP5 climate projections by selecting one realization per model per scenario, and calculating the 5 – 95 % range of the resulting ensemble and the same strategy is generally still used in AR6. Broadly, the following chapters take the CMIP6 5 – 95 % ensemble range as the likely uncertainty range for projections, with no further weighting or consideration of model ancestry and as long as no universal, robust method for weighting a multi-model projection ensemble is available.
1.6.3 Cumulative Carbon Dioxide Emissions
More fundamentally, while a global warming level is a good proxy for the state of the climate, it does not uniquely define a change in global or regional climate state.
2013 IPCC Fifth Assessment Report, The Physical Science Basis
9 Evaluation of Climate Models
Frequently Asked Questions 9.1, Page 824, 3rd paragraph
Are Climate Models Getting Better, and How Would We Know ?
Climate models of today are, in principle, better than their predecessors. However, every bit of added complexity, while intended to improve some aspect of simulated climate, also introduces new sources of possible error ( e.g., via uncertain parameters ) and new interactions between model components that may, if only temporarily, degrade a model’s simulation of other aspects of the climate system. Furthermore, despite the progress that has been made, scientific uncertainty regarding the details of many processes remains.
2007 IPCC Fourth Assessment Report, The Physical Science Basis
8 Climate Models and their Evaluation
Frequently Asked Questions 8.1, Page 601 bottom of 1st column
Nevertheless, models still show significant errors. Although these are generally greater at smaller scales, important large-scale problems also remain. For example, deficiencies remain in the simulation of tropical precipitation, the El Niño-Southern Oscillation and the Madden-Julian Oscillation (an observed variation in tropical winds and rainfall with a time scale of 30 to 90 days). The ultimate source of most such errors is that many important small-scale processes cannot be represented explicitly in models, and so must be included in approximate form as they interact with larger-scale features. This is partly due to limitations in computing power, but also results from limitations in scientific understanding or in the availability of detailed observations of some physical processes. Significant uncertainties, in particular, are associated with the representation of clouds, and in the resulting cloud responses to climate change. Consequently, models continue to display a substantial range of global temperature change in response to specified greenhouse gas forcing (see Chapter 10). Despite such uncertainties, however, models are unanimous in their prediction of substantial climate warming under greenhouse gas increases, and this warming is of a magnitude consistent with independent estimates derived from other sources, such as from observed climate changes and past climate reconstructions.
2001 IPCC Third Assessment Report, The Scientific Basis
14 - Advancing Our Understanding
Page 774, 2nd column, top : 18.104.22.168 Balancing the need for finer scales and the need for ensembles
In sum, a strategy must recognize what is possible. In climate research and modelling, we should recognize that we are dealing with a coupled non-linear chaotic system, and therefore that the long-term prediction of future climate states is not possible. The most we can expect to achieve is the prediction of the probability distribution of the system’s future possible states by the generation of ensembles of model solutions.
1996 IPCC Second Assessment Full Report, The Science of Climate Change,
Section 6, page 24
In particular, to reduce uncertainties further work is needed on the following priority topics:
Representation of climate processes in models, especially feedbacks associated with clouds, oceans, sea ice and vegetation, in order to improve projections of rates and regional patterns of climate change.
1992 IPCC Supplement, Policymaker Summary of Working Group I
(First Scientific Assessment of Climate Change)
Section 5.2, page 75
Although scientists are reluctant to give a single best estimate in this range, it is necessary for the presentation of climate predictions for a choice of best estimate to be made. Taking into account the model results, together with observational evidence over the last century which is suggestive of the climate sensitivity being in the lower half of the range, (see section: " Has man already begun to change global climate ? ") a value of climate sensitivity of 2.5°C has been chosen as the best estimate ( for a doubling of CO2 ).
In this Assessment, we have also used much simpler models, which simulate the behaviour of GCMs, to make predictions of the evolution with time of global temperature from a number of emission scenarios. These so-called box-diffusion models contain highly simplified physics but give similar results to GCMs when globally averaged.