Next Generation Earth System Prediction Strategies for Subseasonal to Seasonal Forecasts (2016) / Chapter Skim
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4 Sources of Subseasonal to Seasonal Predictability
4 Sources of Subseasonal to Seasonal Predictability
Pages 83-116
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From page 83... ...
For weather forecasts, the forecast proceeds from an observed initial state (solid blue circle) out to lead times of a few days.
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From page 84... ...
for the distribution of sea ice and not explicitly include its interaction in the forecast. The influence/interactions of vegetation in today's weather forecasts are often treated similarly, that is, there is reason to believe that they influence key water and energy processes relevant to the weather and climate, but the process models and associated observations for initialization are too incomplete to include in an operational forecast setting.
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From page 85... ...
In this case, our early dynamical ocean model forecasts of tropical SSTs were initialized and run to lead times of 3-12 months to produce future estimates of tropical SST anomalies. These SSTs -- forecast as an initial value problem -- were in turn used as the SST boundary conditions to global atmospheric forecast models run over the same lead times to produce a seasonal climate forecast.
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From page 86... ...
DEFINING PREDICTABILITY In this report, predictability refers to a phenomenon's potential, that is, its upper limit, for being predicted. Theoretically, this is inherent to the phenomenon itself and its limit results from inevitable errors in initial conditions, which are amplified through nonlinear processes in a perfect model.
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From page 87... ...
. Advancements in this research critically hinge on observations, a variety of models, forecast system analogs, and ensemble retrospective forecast data sets.
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From page 88... ...
Each bar is based on one of eight ensemble-prediction systems with varying numbers of members depending on the forecast system, or calculated from approximately 20 years of retrospective forecasts. The black bars denote retrospective forecast skill based on single-member forecasts, while the hatched bar shows the improvement based on the ensemble forecast system -- that is, using the ensemble mean of all the forecast system's members.
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From page 89... ...
. To identify the contribution of a process to forecast skill, numerical retrospective forecasts are usually performed and compared using model configurations with and without the process.
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From page 90... ...
estimated weather noise variance of seasonal means by extrapolating the power spectrum derived from observed daily time series in a season. In the case of a dynamical ensemble retrospective forecast, the ensemble mean represents the "predictable signal component," because it is independent of the uncertainties (in initial condition or model parameter)
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From page 91... ...
-- which only differ by some small perturbation in the initial conditions and/or model parameters -- are used to predict it. These predictability estimates can then be put into the same context as retrospective forecast experiments that instead compare the same predictions to observations in order to quantify forecast skill.
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From page 92... ...
For the purposes of this discussion, we refer to these as "slowly varying processes." The third type of predictability stems from anomalous external forcing that is extensive or strong enough to have an impact globally or regionally for weeks to months (such as cyclic or anomalous solar output, anthropogenic factors, and events such as volcanoes)
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From page 93... ...
If the life cycle is much longer than the S2S timescale, then practically speaking for the purpose of the S2S forecast, the mode's variation would likely be considered a "slowly varying process." Natural modes of variability are often associated with teleconnection properties that relate variability at one location to conditions in another. For example, the mechanisms that produce ENSO occur and evolve in the tropical Pacific Ocean, yet influence midlatitude variability through atmospheric d ynamics.
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From page 94... ...
For seasonal prediction, ENSO's coupled dynamics provides a major source of skill (e.g., NRC, 2010b; Shukla et al., 2000) , while for subseasonal predictions, the relatively slowly varying SST anomalies provide a relatively persistent tropically forced atmospheric circulation anomaly.
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From page 95... ...
phases. Bottom panels illustrate the SST anomalies associated with the ENSO phenomenon, showing both El Niño (left)
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From page 96... ...
. The MJO is mainly an atmospheric phenomenon, but it also exhibits some odest interactions with the upper ocean -- both in forcing and responding to m coupled SST anomalies and exciting ocean currents and waves.
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From page 97... ...
Quasi-Biennial Oscillation The stratosphere is a potential source of S2S predictability because of its persistent and slowly varying circulation anomalies (NRC, 2010b)
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From page 98... ...
. Variability in the annular modes has in turn been associated with episodes of extratropical surface air temperature anomalies (warm spells or cold surges)
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From page 99... ...
Although much progress has been made in understanding these modes, in particular ENSO and MJO, less is known about how the interactions between coupled modes and slowly varying processes influence the development of specific environmental conditions. Continued research into variability in coupled modes, and their interactions across timescales, is necessary to fully exploit their predictability for S2S forecasting.
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From page 100... ...
Furthermore, the threshold for predictive skill must itself have a source of predictability. Even so, the probability of a phenomenon occurring due to persistence in a system with many interacting processes may not be possible to predict with an idealized model or theoretical means, and may require a predictive system, even though the mechanism for predictability at some level a ppears basic and might not be considered "dynamical." Additional details of a number of slowly varying processes within the coupled Earth system that provide predictability on S2S timescales are provided below.
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From page 101... ...
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 (Chelton and Xie, 2010; Chelton et al., 2004. These smallscale variations in sea surface temperature (SST)
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From page 102... ...
. moisture is most likely to be limiting in each region, suggests a The identified regions of strong correlation between surface broader relevance of soil moisture-atmosphere coupling than moisture deficits and temperature extremes are found to be lo- could be assumed from the well-established GLACE study.
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From page 103... ...
The latter occurs because of its high albedo relative to snow-free areas; it acts as a significant surface heat sink via the latent heat required to melt the snow, and in changing the interface conditions it influences the fluxes of heat and moisture between the land and atmosphere. 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 (Jeong et al., 2013; NRC, 2010b; Peings et al., 2011; Thomas et al., 2015)
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From page 104... ...
. Although the mechanisms remain obscure, when global forecast models include more realistic Arctic sea ice and other Arctic variables, forecasts improve in lower latitudes (Jung et al., 2014; Scaife et al., 2014a)
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From page 105... ...
The figure shows that sea ice extent anomalies initially persist for 2-4 months and then, after a period of low correlation, anomalies reemergence at lags of about 6-12 months, depending on the season. The data were de-trended by removing the ensemble mean from the model and a linear fit to the observations.
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From page 106... ...
. Yet for several months following an SSW, enhanced forecast skill has been found in extratropical surface temperatures and sea level pressure (Sigmond et al., 2013)
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From page 107... ...
Over the past decade or so, numerical weather and climate models have started to be able to better reproduce credible seasonal variability through careful representation of the relevant processes. However, significant shortcomings remain in representing the effects from these very well defined external forcings that are highly relevant to S2S prediction, such as the diurnal cycle.
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From page 108... ...
via the change in the runoff and implications for the evolution of the snow pack and the manner it influences weather and short-term climate (see section above on Slowly Varying Processes)
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From page 109... ...
. Finding 4.5: Given the requirement that S2S forecasts have multi-decade retrospective forecast data sets for the purposes of bias correction, is it imperative that the model forecast system account for all slowly varying external forcings that influence the frequency, spatial distributions, and temporal distributions of S2S forecast quantities (e.g., temperature, precipitation)
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From page 110... ...
n It is important to clarify that most of the climate system's 2. Positive equatorial sea surface temperatu warming is taking place within the ocean.
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From page 111... ...
The total number of days (colors) based and 8 and 1 when the amplitude of the tropical cyclones (1975-2011)
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From page 112... ...
Finding 4.6: The nature of sources of S2S predictability, namely intermittent natural modes of variability, wide and often disaggregated variations in anomalous conditions in a number of slowly varying processes/quantities, and varied natural and anthropogenic external forcings, liken the S2S prediction challenge to the identification and successful prediction of a series of "forecasts of opportunity." Identifying such windows of predictability will be particularly important for forecasts of extreme and disruptive events. THE WAY FORWARD FOR RESEARCH ON SOURCES OF PREDICTABILITY The relative value of predictability sources is dependent on location of the forecast and time of the year.
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From page 113... ...
, slowly varying processes (e.g., sea ice, soil moisture, and ocean eddies) , and external forcing (e.g., aerosols)
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From page 114... ...
In summary, accurate prediction of extreme weather/environmental events hinges critically on the accurate representation of all of the dominant modes of variability and slowly varying processes that operate and yield predictability on S2S timescales. Forecast models must represent these processes individually as well as collectively, with specific attention to their multiscale interactions and influences on the development of extreme events.
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From page 115... ...
• Investigate and estimate the predictability and prediction skill of disruptive and extreme events through utilization and further development of forecast and retrospective forecast databases, such as those from the S2S Project and the North American Multi-Model Ensemble (NMME)
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