I have been involved with the E3SM development since I joined PNNL as a postdoc. Over the course of time, I have learnt a lot from the E3SM model. I also found many issues within the model, which reflects lots of similar struggles in the lifespan of software engineering.

Here I list a few major ones that we all dislike but they are around in almost every project we have worked on.

Excessive usage of existing framework even it is not meant to Working in a large project means that you should NOT re-invent the wheels if they are already there. But more often, developers tend to use existing data types and functions even when they were not designed to do so. The reason is simple: it is easier to use existing ones than to create new ones. For example, in E3SM, there was not a data type to transfer data between river and land. Instead, developers use the data type designed for atmosphere and land to do the job. While it is ok to do so, it added unnecessary confusion for future development activities. This type of issue is actually easy to fix. So I created a new type and followed the pattern of other data type. There are lots of details of course but it is the right way to do it.

Too many temporary solutions

Developers usually do not want to spend too much time on how to do a task in a sustainable way. Instead, developers tend to choose the approach that simply works for now. Because we all have lots of deadlines. Some would argue that a working solution is a good solution. While yes and no. For a complex Earth system model, some temporary solutions can cause lots of issues or production delays because they are not sustainable. You might have to spend way much more time to fix the issues than deal with it from the beginning.

Functions can be extremely fragmented if not well designed As a complex model, each module within the system is supposed to do only one task. However, modelers are often not sure how to discretize the processes. For example, snow dynamics involve with calculation of energy balance (solar radiation, ET, etc.). But should we update the snow status after each process? What happens when there is no snow after we calculated shortwave radiation? This challenge is difficult to deal with because it is difficult to have a big picture when you have hundreds of processes going on and no one is an expert on everything. By the model was released, some algorithms were written decades ago and no one really have the resources to keep tracks of everything.

Collaboration requires timely updates With many developers working on different branches, it becomes a challenge to update all the changes with strong dependency. Ideally, development in one branch should not change the behavior of another branch. However, in reality, because of the fragmentation and dependency, we have to spend great effort to make sure they are compatible with each other.

I will keep update this list.