ecoforecast

Great question. First, from the perspective of using forecasts to advance our scientific understanding, we use "near-term" to refer to timescales where we can regularly test our predictions against new data. In some cases these are forecasts that are produced every day like a weather forecast. Others might be weekly, monthly, seasonal, or even annual. But the term is meant to distinguish these forecast from long-term projections (e.g. climate responses in 2100).
Second, your point about the reliability of the weather forecasts is a great one, as those forecasts are indeed a key input into most ecological forecasts. In some cases the weather forecast uncertainties do dominate the uncertainties in our ecological forecasts, which puts a limit on the time which the forecast remains useful. Still, in many cases such short-term forecasts can still be useful to managers, decision makers, and the public (just how weather forecasts are also useful on short scales). This class of forecasts will improve as weather forecasts improve -- indeed, subseasonal to seasonal (S2S) forecasting is a major priority in Earth System predictability research. The other interesting thing is that because many ecological processes integrate over weather variability, ecological forecasts can sometimes be more accurate than the weather forecasts that go into them. As an example, it might not matter to a plant whether it rains today, tomorrow, or this weekend so long as there's enough rain over recent days to weeks that they don't become stressed.

So ecological forecasts can be used to predict crop growth out into the future, which helps farmers anticipate yields, and how they vary from year to year. Importantly, they allow us to anticipant plant stressors (e.g. drought, pests) and implement management options, aiming to do so in ways that are more precise, better timed, and ultimately more cost effectively. At larger scales, forecasts create opportunities for the larger food system to respond to these anticipated changes in yield in different sectors and geographic areas

So you raise a good point that extreme weather events that outside the historical norm (and thus outside the data we use to train our forecasting models) are always going to be a challenge. This is compounded in situations where the weather forecasts for these events are also uncertain. So in that case HOW a forecast model is constructed (which may not be easy to figure out from a website) has a lot to do with how much trust I'd put into it's predictions in these cases. In general, the more a forecast represents our underlying mechanistic understanding of processes, the more likely it is going to be able to extrapolate successfully into new conditions. So for your example of phenology, there's a long tradition of using simple models that just accumulated "warming" or "cooling" which are regrettably not great at distinguishing gradual warming/cooling from extreme events. But I have a graduate student, Kathryn Wheeler, who's been working on forecasts of leaf out and leaf fall whose taken a deeper dive into the physiological mechanisms (e.g. chlorophyll synthesis [creation] and degradation [break down]) and has some new models in the works that we're really excited about. She's currently in the field making measurements as fall hits us here in New England, so fingers crossed 🤞 this Fall's forecast will show improvements.

I do think that the field is heading in that direction -- I've already seen a few academic and non-academic jobs ads that explicitly mention forecasting -- but it could still be a while before forecasting specifically becomes mainstream. That said, I do think that the larger field of environmental data science is growing rapidly, with many universities beginning to offer programs in this area and an uptick in jobs looking for these more broad quantitative skills. Anyone trained in ecological forecasting would be well prepared for these jobs and I suspect anyone hired to do environmental data science will find that forecasting will become a larger and larger part of their job going into the future.

In terms of facilitating job, I'll note that the Ecological Forecasting Initiative ecoforcast.org has a #jobs board in its community Slack. We're not posting those to our website yet (maybe we should!) but right now I'm seeing a lot of jobs being posted that want to hire people from our community relative to the number of folks that are available to fill them.

I'm definitely not the only person working in this area. But one of the things I've been working hard on is helping to bring this small community together into an emerging discipline. Working with others in this area we launched the Ecological Forecasting Initiative ecoforcast.org in 2018 to help build a community of practice that spans the many subdisciplines where folks are working on forecasts (e.g. land, freshwater, marine; biologists, earth scientists, social scientists, computational scientists). That webpage includes a directory of some example forecast research projects throughout the community. We've also been working hard to create conferences, workshops, educational opportunities & materials, and shared tools