Do Self-Aggregation Simulations Depend Crucially on Radiative-Convective Equilibrium (RCE) Initial Conditions?

Do Self-Aggregation Simulations Really Need Radiative-Convective Equilibrium to Get Started?

So, you’re trying to figure out how thunderstorms clump together, right? It’s called self-aggregation, and it’s a big deal for climate models. If we can nail this down, we’ll be way better at predicting future rainfall. But here’s the kicker: do these simulations have to start with this perfectly balanced atmosphere thing called radiative-convective equilibrium, or RCE?

RCE is like the atmosphere’s version of a zen garden – everything’s in balance. Radiative cooling (the Earth losing heat) is perfectly matched by convective heating (thunderstorms churning things up). It’s a neat starting point for simulations. Clean, simple, easy to control. The problem? The real atmosphere? Not so zen. It’s messy, chaotic, and almost never in perfect equilibrium. So, naturally, you start to wonder: are these RCE-based simulations even telling us anything useful?

Some scientists say, “Relax, it’s not that crucial.” They argue that while RCE is a handy starting block, the nitty-gritty details of that initial RCE setup don’t really matter in the long run i. What really drives self-aggregation, they say, are things like how the surface of the Earth is exchanging heat and moisture with the atmosphere, and how clouds are reflecting sunlight back into space. Basically, the big players. The idea is that self-aggregation is a pretty robust phenomenon. Give it the right ingredients, and it’ll happen, regardless of the initial atmospheric mood.

But hold on. Other researchers are waving red flags. They’ve found that those initial conditions can throw a wrench in the works ii. The time it takes for thunderstorms to huddle together, the size of the resulting mega-storm, even whether they huddle together at all can depend on the initial temperature and humidity. Imagine starting with a super-dry atmosphere. Good luck getting any thunderstorms going, let alone getting them to aggregate! Or picture a weird distribution of water vapor high up. That could totally mess with how strong and organized the storms become, and therefore, affect the whole self-aggregation dance.

So, who’s right? Well, it’s complicated, as these things usually are. It often boils down to the specifics of the simulation itself. Think of it like baking a cake. The recipe (the model) matters. A high-resolution model with fancy cloud physics might be less sensitive to the initial conditions. It can handle the chaos and generate its own weather, overcoming whatever initial state you throw at it iii. But a simpler model? That might be more reliant on that initial RCE profile to get things going. It just doesn’t have the chops to create its own variability.

And let’s be honest, “crucially” is a loaded word. Maybe the exact timing of self-aggregation is sensitive to the initial conditions. But does that mean the overall picture – the fact that thunderstorms eventually clump together – is also sensitive? Maybe not. It’s like saying the exact time you leave for work matters, but ultimately, you’re still going to get to work. We need to distinguish between the when and the what.

Bottom line? RCE is a convenient starting point, but it’s not the only game in town. How much self-aggregation depends on it is a mixed bag, influenced by the model’s complexity and what exactly you’re trying to measure. The future? More research, exploring a wider range of starting conditions – ones that look a bit more like the real, messy atmosphere we actually live in. That’s how we’ll truly understand how robust and relevant self-aggregation is to our climate.