Changing Climate or Changing the Global Development Narrative
It has been more than three decades since the ‘global warming and climate change’ debates became part of the mainstream development discourse. 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 that is impacting precipitation, using Global Circulation Models (GCMs) and Regional Circulation Models (RCMs).
But quoting the website of the US Department of Agriculture (USDA) about global climate models and their utility.
- “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), 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.”
“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”.
‘Downscaling’: What does it Yield?
In order to produce climate projections at more local scales (less than 100 km3), models are ‘downscaled’ either using ‘dynamic’ downscaling or ‘statistical’ downscaling. Dynamical downscaling uses the output of GCMs 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. What is important to remember is when models are downscaled, they do not become more “accurate” or better than global climate models, just become more detailed.
The Past and Future of Climate Models
More and better data from the observations of atmosphere and oceans will aid in improving models. Data from the open ocean, etc. are lacking. Without enough observational data, scientists don’t know exactly what outputs from climate models 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.
Reading the above lines carefully makes us realize that modelling of climate using short-term data could be scientifically improper. A far more serious concern is that the modelers generally do not present 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 are often used and hardly efforts are made to obtain country-level data.
Further, how do we reconcile with the fact that in many parts of the world, there is high inter-annual variability in the seasonal rainfall, mean temperature, relative humidity, etc.? How do the climate models handle this additional complexity? As computers become more advanced, models will be able to generate outputs in finer detail. But unfortunately, improved computing will only help to an extent, as long as we are unable to replicate the Earth system perfectly.
Validating Models
In the past 30 years or so, what we have seen is only seen reports and presentations that contain model predictions, with no mention of whether the model is calibrated and validated or not? Further, scientists validate models using past events. 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. After all, scientists test a model’s accuracy using past events.
The trends that the models predict are also unidirectional. Then the question is “If the model simulates a complex system that takes into account the interactions between the ocean, atmosphere and land, how come the trend remains the same for different time periods. Ideally, with the value of each input variable changing with time, their dynamics of interaction could also change.
Now let me present the analysis of rainfall data from select regions of India.
- The analysis for Kachchh in Gujarat) shows increasing annual rainfall during the period from 1901 to 2021, with an average rise of 1.3mm per year.
- The analysis for Karbi Anglong district in Assam shows decreasing annual rainfall during the period from 1901 to 2023, with a substantial reduction of 3.96mm per year.
- The analysis for Purulia district of West Bengal shows increasing annual rainfall during the period from 1901 to 2023, with an average rise of 0.90mm per year.
This varying trend is difficult to explain. 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.
Now, the usual argument for using the ensemble models which take the mean of the results from several models is that this would increase the confidence level. This is totally incorrect. 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 is which model to use for future water resource planning.
Concluding Remarks
The new narrative of ‘changing climate’ forces the developing countries believe that what they have been doing to manage their land, water and energy resources in the past is no longer going to be effective. With this, the global development narrative is changing. Hence the developing are under pressure to divert a significant chunk of the limited resources available for poverty alleviation for climate action. To reduce the opportunity costs of doing this, the climate scientists should educate us of how the Earth system is changing with a certain degree of confidence and what the best actions are to mitigate the changes and to adapt.
Writer: M. Dinesh Kumar, Executive Director, the Institute for Resource Analysis and Policy.
