Next Generation Earth System Prediction- Strategies for Sub Seasonal to Seasonal Forecasts

The National Academy of Sciences’ (NAS) Board on Atmosphere held a briefing on the report “Next Generation Earth System Prediction: Strategies for Subseasonal to Seasonal Forecasts.”  The full report, or report in brief are both available to download. Raymond Ban of Ban Associates, LLC, and chairman of the report’s authoring committee, presented the report’s highlights.

The Office of Naval Research (ONR), NASA, and the Heising-Simons Foundation had asked NAS, Engineering, and Medicine to undertake a study to develop a 10-year U.S. research agenda to increase subseasonal to seasonal (S2S) research and modeling capability, advance S2S forecasting, and aid in decision making at medium and extended lead times.

Ban said that because many critical planning and management decisions are made weeks to months in advance, improved S2S forecasts could better inform those decisions, which in turn can help save lives, protect property, increase economic vitality, and protect the environment.

S2S forecasts are defined as those made two weeks to 12 months in advance.  For example, planning for naval and commercial shipping routes could take advantage of favorable conditions predicted weeks in advance.

The report’s purpose was to describe a strategy to increase S2S forecasting skills in order to guide progress towards the committee’s vision of S2S forecasts being used in a decade as widely as weather forecasts today.  Weather is defined as predictions between zero and 14 days.

The committee proposed four research strategies and 16 recommendations for accelerating progress on S2S forecasting.

The four strategies are:

1. Engage users in the process of developing subseasonal to seasonal forecast products.
2. Increase subseasonal to seasonal forecast skill.
3. Improve prediction of extreme and disruptive events and of the consequences of unanticipated forcing events.
   1. Unanticipated events are in of themselves a source of predictability.
   2. Events that are so large they can change climate need to be played out in advance scenarios so that future impacts can be predicted.
4. Include more earth system components in subseasonal to seasonal forecast models.
   1. Calculate the probability of opened Arctic shipping lanes.
   2. Arctic conditions and mid-latitude conditions have a clear connection, and the research community has recognized that multi-modal ensembles make better predictions. With a deliberate design and more support for a systematic approach, S2S forecasting will improve.

Ban said the report’s real take home message, based on feedback from end users, is that the ability to preplan and have options in the S2S timeframe creates significant value.  This is in harmony with the committee’s first strategy to engage users.  Another end user comment was a request for predictions beyond the usual temperature and precipitation, and the incorporation of other parameters.

The following is a summary of other key Arctic highlights in the report.

NATIONAL SECURITY & DEFENSE

S2S forecasting could prove particularly beneficial on a routine basis in the area of global ocean and ice predictions, particularly as the Arctic warms.  In addition, advance disaster preparations that may involve a military response could benefit from relief effort pre-staging at designated sites around the world.  S2S predictions on food and water can contribute key information to national security, and provide insight into possible droughts, floods or famine.

The defense sector makes important decisions regarding military operations which involve advance warning of environmental conditions on S2S timescales.  The decisions include vessel routing, military exercise planning, war games, tactical planning, disaster relief, and search and rescue advance planning (e.g., in the Arctic).  In addition, extreme weather events can threaten military facilities in vulnerable locations.

Better-integrated predictions of sea ice in a changing Arctic are needed.  The

Navy and Coast Guard have focused attention on Arctic via their 2014 Roadmap and 2013 Strategy, respectively (US Navy Task Force Climate Change, 2014; USCG, 2013).

Arctic installations, in fact, are considered some of the most vulnerable.

“The combination of thawing permafrost, decreasing sea ice, and rising sea level on the Alaskan coast have led to an increase in coastal erosion at several Air Force radar early warning and communication installations.  According to installation officials, this erosion has damaged roads, utility infrastructure, seawalls, and runways…..As a result, only small planes or helicopters are able to land in this location, as opposed to larger planes that could land on the runway when it is fully functional.  Daily operations at these types of remote radar installations are at risk due to potential loss of runways, and such installations located close to the coastline could be at risk of radar failure if erosion of the coastline continues.  Air Force headquarters officials noted that if one or more of these sites is not operational, there is a risk that the Department of Defense early warning system will operate with diminished functionality.”  (GAO, 2014)

For the Coast Guard, medium-range (subseasonal) response planning in the event of an accident in Arctic seas (for example, an oil spill or a cruise ship evacuation) is needed.  Navy forces are also much more likely to be engaged in the Arctic to assist

Coast Guard search and rescue and other civil support operations (U.S. Navy Task Force Climate Change, 2014).

Within this context, better forecasts are needed now, but the demand will be particularly great as climate change begins to initiate ice free passage through the Arctic Ocean and activity related to shipping, tourism and energy extraction increases.

In the Arctic, a number of S2S prediction system features can be advanced to produce more skillful S2S forecasts.

 

Predictability

Predictability derives from a number of processes and phenomena that exhibit a wide range of timescales.  The report considers three general types: (1) recurring patterns of variability, (2) slowly varying processes, and (3) anomalous external forcing that is extensive or strong enough to have an impact globally or regionally for weeks to months.

Understanding and modeling the dynamics of each of these predictability source types, as well as their interactions and teleconnections, is essential to generating S2S forecasts.

 

PREDICTABILITY FROM SLOWLY VARYING PROCESSES

S2S predictability can stem from persistence in the initial state of various components of the Earth system, which often stems from storage of anomalous energy in the form of heat or water in a given phase, including snow, sea ice, soil moisture, or ocean heat content.

When these anomalous heat stores occur on large spatial scales their dissipation typically occurs over several days, weeks, or months, and thus provides the Earth system with predictability.  Smaller anomalies may also provide predictability for important ocean and coastal properties that are of interest on their own.  Oceans, terrestrial snow, and sea ice and polar land surfaces provide predictability on S2S timescales.

 

Ocean

The ocean represents a key source of predictability on S2S timescales.  The report focused on mechanisms involving ocean surface conditions, owing to their relevance for humans that included large and small-scale ocean dynamics in the tropics, as well as ocean interactions with the atmosphere and sea ice through surface exchange of energy, moisture, and momentum, yielding both one-way influences and coupled feedbacks.

The persistence of surface anomalies depends primarily on the depth of the upper ocean mixed layer.

Small-scale (10s-100s of kilometers) surface ocean features, such as circular motions known as eddies and regions of strong gradients known as fronts, can also exhibit persistence for months to years.  These small-scale variations in SST cause divergence and convergence in the surface wind and vertical motions that link the small-scale ocean features to cloud properties and other atmospheric features.

Ocean eddies also have an association with ocean biogeochemistry through their influence on upwelling or downwelling, horizontal advection, and isolation of nutrients and ecosystems.  Because of their persistence and coupling with the atmosphere, these eddies represent a potential source of S2S predictability for the ocean and even the entire Earth system if feedbacks to the atmosphere are prominent.

 

 

 

Terrestrial Snow

Snow also contributes to predictability of atmospheric and land conditions.  This is through its storage of surface water and its influence on surface energy budgets due to its high albedo relative to snow-free areas, which means its acts as a heat sink because of the large amount of latent heat required to melt the snow.

Additionally, knowledge of anomalous snow conditions, particularly the snow water equivalent as opposed to just snow cover, can improve forecasts of air temperature and humidity, runoff, and soil moisture during the winter and spring seasons.  For large-scale anomalies in snow conditions, there is also some evidence that snow can influence remote atmospheric conditions by altering large-scale atmospheric circulation features.  For example, correlations have been documented between autumn anomalies in Eurasian snow and the large-scale northern hemisphere atmospheric circulation a few weeks to months later through the influence of snow cover on the vertical propagation of wave energy into the stratosphere and the North Atlantic Oscillation.

Snow cover and snow water can have a profound influence on the evolution of the local, regional, and even large-scale weather patterns as well as a number of Earth system components.  This influence places a high priority on ensuring observations of snow are available for process understanding and forecast and that the terrestrial hydrology and atmospheric models properly represent snow and related processes.

 

Sea Ice and Polar Land Surface

Sea ice lends predictability to the Earth system because its presence strongly reduces heat and moisture fluctuations from the ocean to the atmosphere.  It serves as a significant reservoir of freshwater within the upper ocean, and it is an excellent reflector of solar radiation.

The persistence of sea ice anomalies has several important timescales.  There is an initial persistence of anomalies in the sea ice cover that varies from two to four months, depending on the season and location.

After this initial period of persistence, a reemergence occurs in some seasons owing to sea ice internal dynamic and coupled interactions between sea ice and sea surface temperature (SST).  Modeling studies suggest anomalies of sea ice thickness are far more persistent and about as important as SST in controlling the persistence characteristics of the sea ice cover.

The lack of long-term sea ice thickness measurements forces researchers and forecasters to turn to models to estimate these quantities.  When models factor in transport, sea ice thickness anomalies can persist for almost two years and exhibit typical length scales of about 500-1,000 km.

The multitude of interactions involving the mid-latitude jets has made it difficult to find conclusive evidence of Arctic-mid-latitude weather linkages.  But, though the mechanisms remain obscure, when global forecast models include more realistic Arctic sea ice and other Arctic variables, forecasts improve in the lower latitudes.  Because of the persistence of sea ice and Arctic snow cover, an improved understanding of sea ice and related processes and the mechanisms linking Arctic and mid-latitude conditions is important.  These processes and mechanisms should also be incorporated into models used for S2S predictions.

 

PREDICTABILITY FROM EXTERNAL FORCING

While the lifetimes of other atmospheric constituents can be much longer, it is still critical that they be accounted for in S2S forecast systems.

A notable example of this is the concentration of greenhouse gases (GHGs).  The typical lifetime of anthropogenic greenhouse gas anomalies is on the order of a decade to centuries, and fluctuations and trends in the emissions of greenhouse gases also tend to occur on timescales that are long relative to the S2S forecast.

These long timescales imply that, for a given forecast, the GHG concentration can be specified to be a constant.  But, multi-decade retrospective forecast datasets need to be included in S2S forecast systems because of bias correction, so values of the impactful constituents must be specified to the forecast system as a time-varying boundary condition over the time period of the forecasts.  This type of slowly varying forced signal can lead to systematic shifts in the probability distributions of variables like temperature and precipitation that can be predicted given the known value of the forcing.

Furthermore, such external forcing has caused the seasonal minimum of Arctic sea ice extent to decline by over 40 percent, radically changing the probability of where the sea ice edge lies at the end of summer in recent years compared to the beginning of the satellite record in 1979.  As S2S forecast systems encompass more Earth system components and coupled processes that are influenced by such external forcing, it is important to have an accurate representation of GHG forcing and other slowly varying external forcing (e.g. solar constant, surface albedo).

 

FORECAST SYSTEMS CAPABILITIES, GAPS, AND POTENTIAL

Certain features of S2S prediction systems should be advanced in order to produce more skillful S2S forecasts.

The functioning architecture of a typical S2S system consists of producing forecasts in many similar ways to contemporary numerical weather prediction, observations provide initial conditions for computing the evolution of these systems forward in time.

But there are several differences:

• Chaotic aspects of the Earth system mandate averaging S2S predictions over long enough periods to produce stable forecast statistics for each place and lead time
• A set of similar forecasts made in retrospect for 20 or more years within the same forecast system is compared with verification observations to calibrate the forecasts in order to correct the predicted probability distribution on the basis of how the model reproduces past conditions
• Longer timescale predictions include interactive Earth system components like ocean and sea ice since the evolutions of these components have their own right

The production of calibrated subseasonal and seasonal forecasts involves three separate processes:

1. Historical observations over a period of two or more decades are combined with data assimilation and Earth system dynamics in a coupled Earth System computer model to produce a reanalysis that is a detailed history of a wide variety of variables.
2. The reanalysis data is sued as the initial conditions for a set of retrospective ESM S2S forecasts over the same two decades or more.
3. Current observations processed with data assimilation serve as the initial conditions for a set of operation forecasts that are then calibrated in the prediction system and turned into S2S forecasts.

Routine observations focus on observations for operational model initialization, calibration, evaluation, and routine monitoring.  Generally the most basic quantities are needed with continuous temporal and broad spatial coverage, and at spatial and temporal resolutions that are relevant for S2S processes.

The observing system of the cryosphere and Polar Regions depends on unique observing methods to confront the challenges of taking measurements in extreme and harsh environments.

Much of the important phenomena for S2S polar prediction contain small spatial scales, such as the high degree of spatial variability associated with melt ponds, openings in sea ice, patchiness of snow cover, and eddies in the ocean.  The high reflectivity of ice and snow surfaces on land and ocean, lack of strong horizontal and vertical temperature gradients, and the extended polar night make atmospheric observations difficult from passive radiances, e.g., visible measurements based on sunlight reflected from clouds or snow, or infrared measurements based on thermal contrasts.  Further, sea ice is a barrier to most ocean-observing satellites.

As a result, routine in situ observations are critical to complement satellite observations around the poles, in particular for ocean observations.  Traditional field-based measurements are also hindered by the presence of sea ice and a shortage of population centers from which to operate or launch instruments.

Sea ice concentration is one of the most essential variables for predicting weather and climate in the Polar Regions.  It can be measured by passive microwave retrievals (the same satellites that observe terrestrial snow cover) through clouds and during both day and night.  Passive microwave retrievals also can be used to distinguish first-year and multiyear ice.  These observations are available since 1979 and provide the only continuous coverage of sea ice longer than a decade.

However, high uncertainty in sea ice concentration measurements is found when melt water is present at the surface and resolutions are relatively coarse (~10 km).

Sea ice thickness is less well observed than sea ice concentration, but it is at least as important for sea ice prediction, as it is a key constraint on the timescale of variability (~ months to years) for sea ice concentration anomalies.  For example, summer sea ice coverage—a variable that is often a target for prediction—is strongly influenced by sea ice thickness in spring.  Scattered field based measurements of sea ice thickness are available since the late, and in the last two decades a series of satellites and aircraft have provided good spatial coverage but not continuously; in some cases instruments were turned off to extend the life of the mission (ICE Sat) and in others melt water on the surface obscured the measurements in late spring and summer (IceBridge, CryoSat-2).

At present, the only thickness-observing satellite is CryoSat-2, operated by the European Space Agency, which has been in orbit since 2010.  Because remote sensing actually measures the freeboard (height of sea ice and snow above sea level), the accuracy of estimates of sea ice thickness depends critically on the availability and quality of measurements of snow depth on top of the sea ice.

The lack of simultaneous measurements of snow depths and freeboard leads to significant uncertainty in the estimate of thickness, but even more problematic for S2S forecasting is the impossibility of retrieving data from the radar altimeter instrument on CryoSat-2 (and CryoSat) in the presence of surface meltwater, or roughly May-September in the Arctic.  Nonetheless, CryoSat thickness measurements have been used for sea ice data assimilation to initialize forecasts in spring of the ensuing summer season (see section on data assimilation).

NASA’s IceBridge aircraft mission offers one of the best opportunities to measure simultaneous freeboard and snow depth, though the measurements are limited to about a dozen flight tracks each year over a few weeks in spring since 2007.  Even in these opportune conditions, the uncertainty in IceBridge sea ice thickness is estimated to still be 40 cm.

Less accurate snow depths have been estimated for the purpose of computing sea ice thickness from satellite based measurements of freeboard in a variety of ways, including from climatological measurements, accumulation of snowfall from reanalysis, and using an empirical method based on ice type and climatological measurements.  However, the accuracy of resulting sea ice thickness was not reported in these studies.  Recently, snow depths have also been estimated from the SMOS satellite mission to be nearly as accurate as the IceBridge measurements, which is very encouraging.

NASA plans to launch a satellite known as the second generation Ice Cloud and Land

Elevation Satellite (ICESat2) in 2017 that can measure sea ice thickness year round, but accurate and simultaneous snow depth measurement are still necessary to fully utilize these observations.

Further, the data need to be processed within a day or so of the observation to be useful as input for prediction of the sea ice edge at shorter lead times in S2S forecasts.

 

CONCLUSION

The report envisions a substantial improvement in S2S prediction capability and expects valuable benefits to flow from these improvements.

Despite the specificity of the report in recommending what should be done, it does not address the challenging issues of how the agenda should actually be pursued, who will do the work, and how the work will be supported financially.

Given that this research agenda significantly expands the scope of the current S2S efforts, the committee believes that some progress can be made with current levels of support within current organizational structures.  But fully achieving the S2S vision will likely require additional resources for basic and applied research, observations, and forecast operations.