(Yesterday’s LOTRW post introduced the subject of transition documents. Today’s post continues that train of thought.)
Yes, a superbloom is a thing. Of course it is! This is the 21st century, and we like to supersize everything, from our hamburgers to our storms. Let’s start with a factoid: in the southwestern United States, perhaps as many as 10,000 seeds can lie dormant in each square meter of desert (!!). Each seed patiently awaits the perfect set of moisture conditions – in short, enough for the plant not just to germinate, but for the resulting plant to live out its full life cycle and reproduce. When the needed conditions align, the result is a superbloom.
And yes, the predictability desert is also a thing. But it’s not a place. It’s a time gap. It separates the several-day horizons of weather forecasts and the decadal outlooks of long-term climate forecasts. Weather forecasts have improved as measurements of initial conditions have improved with respect to global coverage, space and time resolution, and accuracy; as data assimilation has become more rigorous; and as numerical weather prediction (sheer computing power; computational methods; ensemble techniques, etc.) have advanced. Similarly, in recent years, climate forecasting on time scales of years to centuries have improved as the governing boundary conditions have been better characterized. But improvement in weather prediction on intermediate time scales – ranging from a few weeks to a few seasons or so – has lagged. Hence the emergence of the predictability desert.
There doesn’t seem to be a single missing piece to the puzzle. Rather it’s been widely recognized that a variety of improvements need to be stitched together to make progress. Here’s a list of research priorities the World Meteorological Organization published a few years back,
- Understanding the mechanisms of subseasonal to seasonal predictability.
- Evaluating the skill of subseasonal forecasts, including identifying windows of opportunity for increased forecast skill, with special emphasis on the associated high-impact weather events.
- Understanding model physics and how well the important interaction processes in the Earth system are represented.
- Comparing, verifying and testing multi-model combinations from these forecasts and quantifying their uncertainty.
- Understanding systematic errors and biases in the subseasonal to seasonal forecast range.
- Developing and evaluating approaches to integrate subseasonal to seasonal forecasts into applications.
The WMO buttressed that list with a few examples of processes that could improve predictability, if better incorporated into the NWP:
- The Madden-Julian Oscillation: as the dominant mode of intraseasonal variability in the tropics that modulates organized convective activity, the Madden-Julian Oscillation has a considerable impact not only in the tropics, but also in the middle and high latitudes, and is considered as a major source of global predictability on the subseasonal time scale;
- Soil moisture: inertial memory in soil moisture can last several weeks, which can influence the atmosphere through changes in evaporation and surface energy budget and can affect the forecast of air temperature and precipitation in certain areas during certain times of the year on intraseasonal time scales (e.g. Koster et al. 2010);
- Snow cover: The radiative and thermal properties of widespread snow cover anomalies have the potential to modulate local and remote climate variability over monthly to seasonal time scales (e.g. Sobolowski et al. 2010);
- Stratosphere-troposphere interaction: signals of changes in the polar vortex and the Northern Annular Mode/Arctic Oscillation (NAM/AO) are often seen to come from the stratosphere, with the anomalous tropospheric flow lasting up to about two months (Baldwin et al. 2003); and
- Ocean conditions: anomalies in upper-ocean thermal structure, in particular sea-surface temperature, lead to changes in air-sea heat flux and convection, which affect atmospheric circulation. The tropical intraseasonal variability forecast skill is improved when a coupled model is used (e.g. Woolnough et al. 2007), while coupled modes of ocean-atmosphere interaction, including the El Niño–Southern Oscillation in particular, can yield substantial forecast skill even within the first month.
The problem is not a new one; it’s been recognized – and resisted efforts at improvement – for a while. This lack of progress matters. Good forecasts in this time range would support more effective agriculture, water resource management, and energy production and use; they could also reduce disaster impacts and improve public health. Benefits would not be confined to the United States but extend worldwide.
Good news! The time just might be right for such forecast improvement. Both the needed technological and political conditions seem to be aligning.
Start with technology. (1) New observing tools continue to come on line, promising to expand geographic coverage into currently under-monitored regions of the world (the oceans, polar latitudes, and the developing world), improve time resolution, and enrich diagnostic power and accuracy. (2) We stand on the threshold of exascale computing, the better to explicitly incorporate additional observations and physical processes into the models. (3) Artificial intelligence is poised to make significant contributions to data quality control and model interpretation, to tease out new connections that influence oceanic and atmospheric developments in sub-seasonal to seasonal time frame, and that translate these changes in environmental conditions into human impacts.
Then there’s the favorable politics. (1) Here in the United States, there’s bipartisan political support for more work on sub-seasonal to seasonal forecasts, as reflected in the Weather Research and Forecast Improvement Act of 2017 and in its subsequent reauthorization as part of the NIDIS 2018 reauthorization. (2) The World Meteorological Organization is emphasizing similar improvements as part of its Global Framework for Climate Services. (3) Observations and study of the oceans – a key piece of the puzzle – have been targeted for special attention by the Intergovernmental Oceanographic Commission, which is launching a Decade of Ocean science for Sustainable Development.
Why dwell on this, and why entertain an approach to a transition document along these lines? Three arguments.
First, the transition document immediately becomes something more than mere pleading from a special interest group. It doesn’t look like a laundry list of policy priorities designed to make the Earth observations, science, and services community well. Instead, the focus is on a single grand national challenge – much like the Manhattan Project during World War II or the effort to put men on the moon during the 1960’s.
Second, the oasification (yes, that’s a thing, too) of the predictability desert will necessarily be most evident at the fringes. On the one end, it will reflect progress on, and at the same time contribute to other U.S. national initiatives on short term prediction such as EPIC. On the other, it will enable earlier detection of skill and flaws in longer-term climate predictions. Its influence will be felt across the entire spectrum of environmental intelligence and related security- and economic concerns.
Third, it’s impossible to contemplate such a venture without paying attention to a range of infrastructure needs facing our community, as detailed in transition documents from prior election cycles. Requirements for modern, more capable observations and computing infrastructure. Attention to workforce trends. Social science ranging from risk communication to economic valuation of the new products and services. But now these are seen for what they truly are – means to a larger, desired national end, versus ordinary welfare appeals.
Advancing seasonal-to-sub-seasonal forecasts provides just one example of a possible organizing goal; there are others. As our community looks ahead to articulating a vision for the next administration, it would be useful to hear your proposals and views.