Changing Climate or Changing the Narrative?

M. Dinesh Kumar

It has been more than three decades since the ‘global warming and climate change’ debates became part of the mainstream development discourse worldwide. The ‘climate change diehards’ argue that our blue planet is warming due to greenhouse gas emissions and because of that the Earth’s climate is changing. With the setting up of the Intergovernmental Panel on Climate Change (IPCC) in 1988, studies are being done periodically to keep the world abreast of how the Planet’s atmosphere is warming up and how, as a result, the climate is changing in different parts of the world.

Several models (Global Circulation Models and Regional Circulation Models) are employed by researchers world-wide, especially those from the developed world for predicting temperature and precipitation for different levels of GHG emission.? There is a rush for developing new climate prediction models and then ensemble models to predict the ‘doomsday’, as if the world is going to end without it. The climate change exponents pretend that these models can never be wrong.

But let me quote what the US Department of Agriculture says about global climate models and their utility in its website.

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• “Global climate models are computer programs that consist of several hundred thousand lines of code. They calculate the interactions between the ocean, atmosphere and land using factors such as water vapor, carbon dioxide, heat, and the Earth’s rotation as inputs.
• Climate models project?climate?(the average weather over a long period of time, e.g., a 30-year period), not?weather?(what an area experiences on an hourly or daily basis).
• Climate model outputs are very coarse, or low resolution. To see outputs at a more local scale, you must look at the downscaled version of the model.”

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“Climate models calculate the physical interactions between four components of the earth system: atmosphere, land, ocean, and sea ice. The calculations are based on several inputs: air temperature, pressure, density, water vapor content, and wind magnitude”.

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‘Downscaling’: What does it Yield?

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There is a lot of myth about ‘downscaling’ of climate models. In order to produce climate projections at more local scales (less than 100 km3), models are “downscaled”. There are two methods of downscaling: ‘dynamical’ and ‘statistical’. Dynamical downscaling uses the output of global climate models as the input for finer-scaled regional climate models that recalculate climate at a finer scale using local features. Statistical downscaling uses statistics to show how large-scale?climate patterns affect the local climate. The website cautions: “Keep in mind that when models are downscaled, they do not become more “accurate” or better than global climate models, just more detailed”.

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The Past and Future of Climate Models?

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The website of USDA says: “More and better observational data on the atmosphere and oceans will aid in improving models. Data from certain parts of the planet are lacking, such as from the open ocean. Enough data exist to make robust projections about the climate, but more data would improve models. Without enough observational data, scientists don’t know exactly what outputs should look like. This makes it hard to know?if?a model is wrong,?why?a model is wrong, and how to improve it. Also, climate models are currently based on observed data from the past few decades. However, the Earth's climate is constantly changing, so it is important to use more recent data to create models. With more information, we can understand what changes are happening in the present climate, and how that climate will change in the future”.

If we read the above lines carefully, we will realize that all that we are doing in the name of climate modelling could be scientifically improper. For instance, based on data for past 30 years, can we now make a long-range prediction of the temperature, vapor pressure and rainfall for the year 2100? A far more serious concern is about the kind of data being used by the modelers. The modelers, who are in a haste to show their outputs, generally shy away from presenting the input data. So we don’t even know whether the data used is long-term or short-term, the degree of resolution, and the authenticity. It seems, standardized datasets (ocean surface temperature, ice concentration, land use, carbon emission, etc.) are often used and hardly efforts are made to obtain country-level data available from various national agencies. ?

Those issues notwithstanding, how do we reconcile with the fact that in many parts of the world (especially in the hot and arid tropics), there is high inter-annual variability in the seasonal rainfall, mean temperature, relative humidity, etc.? How do the climate models handle this additional complexity? Are we satisfied with a sweeping statement from the modelers that the rainfall extremes and variability would increase in future? ??

The agency (USDA) is quite transparent about the limitations of climate models. “Improvements to computing power will also improve models. As computers become more advanced, models will be able to output information in finer detail”. But all that will help only if we decide to use the local climate data that is validated. Again, as the website puts it correctly: “Unfortunately, improved computing will only help to an extent, as we still cannot replicate the Earth system perfectly”. So my request to climate modelers: “kindly talk also about the limitations of your model results”.

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Calibrating and Validating Models?

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In the past 30 years or so, I have only seen reports and presentations that contain model predictions, with no mention of whether the model is calibrated and validated or not? This clearly shows that most of those who are running the models do not have any confidence in the working of the model I would go to the extent of saying that they don’t even know the various physical processes of the Earth system that the model tries to simulate. Since we have been doing model predictions for more than 30-35 years, comparing predicted values of the climate variables with observed values should not be a problem. But this is never done!

After all, scientists test a model’s accuracy using past events. If the model accurately predicts past events that we know had happened, then it should be pretty good at predicting the future, too. And the more we learn about past and present conditions, the more accurate these models become.

Interestingly, the model predictions are unidirectional. If rainfall prediction for next 20 years shows declining trend, the predictions for the next 100 years would also show declining trend. Then the question that should come to our minds is “If the model simulates a complex system that takes into account the interactions between the ocean, atmosphere and land using factors such as water vapor, carbon dioxide, heat, etc., how come the trend remains the same for different time periods. Ideally, it shouldn’t. The reason is that as the value of each input variable changes with time, the dynamics of interaction among different variables could also change.

Now let me present the analysis of rainfall data from select regions of India.

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• The analysis of rainfall data for Kachchh in Gujarat (an arid region in western India) shows increasing annual rainfall during the period from 1901 to 2021, with an average rise of 1.3mm per year.

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• The analysis of rainfall data for Karbi Anglong district in Assam (high rainfall, sub-humid area) shows decreasing annual rainfall during the period from 1901 to 2023, with a substantial reduction of 3.96mm per year.

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• The analysis of rainfall data for Purulia district of West Bengal (a semi-arid, high rainfall area) shows increasing annual rainfall during the period from 1901 to 2023, with an average rise of 0.90mm per year

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How do we explain this varying trend? Even within the high rainfall region (Assam and West Bengal), the trend is not similar. Further, within the same location, the trend changes depending on which time segment one chooses. For instance, in Purulia, the first few decades show decreasing trend and the remaining decades show increasing trend. In Kachchh, the trend during 1901 to 1959 was rising; it became declining during 1959-91; and then again became rising during 1991-2021.

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Logic behind Ensemble Models?

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Coming to ensemble models, one is clueless as to how they produce better outcomes. Let us take 10 different models, of which five predict increased rainfall for a region for 2050, and the other five predict reduced rainfall for the same region--with varying extents of change. The usual argument for taking the mean of the results is that this would increase the confidence level of the model outputs. This is totally incorrect. The reason is ‘what if the first five models are actually wrong (i.e., not simulating the climate correctly) and the last five simulate the earth system with relatively higher accuracy. In that case, our final results will be distorted if we take the average.

The?CMIPS (Coupled Model Intercomparison Project) analyzed 39 different climate models that provide estimates of precipitation changes in the future. Unlike for temperature, where models show a general degree of agreement about future regional changes, different models may show the same region becoming much wetter or much drier in a warming world. For example, the Australian?CSIRO?model projects precipitation decreases of around 50% on average by the end of the century. In stark contrast, the Chinese?FGOALS?projects a 30% average increase in precipitation by 2100, with almost no areas experiencing less rainfall. Now the question before the water policy makers of Australia is which model to use for future water resource planning.

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Concluding Remarks

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The science of climate is complex; more complex is the science of climate change. Climate scientists should educate us of the changes that the Earth system is witnessing (with certain degree of confidence) and what are the best actions to mitigate the changes and to adapt. The ‘climate advisories’ should not be based on half-cooked information that only create confusion. Alas, the advocates of climate change seem to be creating a new narrative that would force us to believe that what we have been doing to manage our land, water and energy resources in the past will no longer hold good.

We must remember that every climate action has a huge opportunity cost. The developing countries are under pressure to divert a significant chunk of the limited resources that are available for improving water, energy and food security, ecological restoration and poverty alleviation programmes for climate mitigation and adaptation. Therefore, the proponents should provide scientific evidence to substantiate their claims. They should first confirm that their predictions of past events are quite close to what had happened. While we understand that the science of climate change is still evolving and models can become more accurate over time, rushing to conclusions on what would happen in future is unacceptable.?? ???

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M. Dinesh Kumar is Executive Director of Institute for Resource Analysis and Policy (IRAP), Hyderabad. The views expressed in this article are personal. Email for correspondence: [email protected] ???