Next Generation Earth System Prediction Strategies for Subseasonal to Seasonal Forecasts (2016) / Chapter Skim
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5 S2S Forecast Systems: Capabilities, Gaps, and Potential
5 S2S Forecast Systems: Capabilities, Gaps, and Potential
Pages 117-206
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From page 117... ...
The production of probabilistic forecasts on S2S timescales is similar in many ways to contemporary numerical weather prediction: observations of the atmosphere, ocean, cryosphere, and land provide initial conditions for computing the evolution of these Earth system components forward in time. However, there are some important differences between S2S and shorter-term weather and ocean prediction.
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From page 118... ...
In the second phase, the reanalysis data is used as the initial conditions for a set of retrospective ESM S2S forecasts over the same two decades or more. The comparison of the retrospective forecasts with an appropriate set of verification data (perhaps the reanalysis)
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From page 119... ...
In order to be integrated into the model state space, these observations must first be transformed via data assimilation, a process that attempts to optimally combine observations with a short-term (usually less than a day) model forecast using the error characteristics of each observation type.
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From page 120... ...
An expansive network of in situ and remotely sensed observing systems is used for S2S forecasting. However, maintaining this network to ensure no degradation of present-day nascent S2S forecast skill represents 1 As noted in Chapter 1, other commonly used terms for retrospective forecasts are "reforecast" and "forecast history." These terms are interchangeable.
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From page 121... ...
This is because, although the ocean, land surface, and cryosphere contain important sources of Earth system predictability on S2S timescales, observations within these components are neither as numerous nor as distributed as observations of the atmosphere. Recommendations and priorities for observations to support S2S forecast systems are presented at the end of the section.
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From page 122... ...
Measurements from aircraft are mostly limited to flight level except near airports. These gaps in spatial coverage are a particular concern for S2S prediction because they span regions through which signals from phenomena over the tropical ocean (e.g., the Madden–Julian oscillation [MJO]
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, and Advanced Scatterometer (ASCAT) derived surface wind speeds.
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From page 124... ...
Given the uncertainties about the future of the radiosonde network and gaps in its coverage, continued investment into satellite-based atmospheric observations is important for moving forward. The development of platforms and algorithms for the retrieval of key variables -- including vertical profiles of temperature, humidity, and wind -- at resolutions that can capture the development and evolution of mesoscale systems and more detailed information in the boundary layer are particularly important.
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From page 125... ...
Such measurements could also improve the representation and initialization of soil moisture within the land surface component of current S2S prediction systems (see below)
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From page 126... ...
Accurate initial conditions for SST as well as for ocean currents are not sufficient for predicting the time evolution of SST on S2S timescales because the effective ocean heat capacity on S2S timescales depends strongly on how deeply surface thermal anomalies are mixed by near-surface winds, ocean surface waves, and convective instabilities in the ocean mixed layer. Thus measurements of winds, waves, air-sea fluxes, 126
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From page 127... ...
, and scatterometerderived surface wind stress are routinely used by ocean prediction systems. However the value of remotely sensed measurements for S2S forecasting depends critically on having enough instruments to provide continuous measurements with adequate temporal and spatial coverage.
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From page 128... ...
However, because SWOT is a research satellite, its 3-year projected mission lifetime is shorter than is desirable for operational use as a part of a well-validated S2S forecasting system. Although there is a foundation for remotely sensed SSH measurements via Jason and the European Sentinel program, there is great concern regarding the continuity of surface wind observations over the ocean.
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From page 129... ...
Satellites also estimate ocean surface salinity (i.e., from the Soil Moisture and Ocean Salinity [SMOS] 7 and, until recently, Aquarius missions8)
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, including the large ongoing 2015-2016 El Niño. For tropical ocean surface moorings to continue to benefit operational ocean and S2S forecasting, they need to deliver consistent and reliable observations.
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provides synergistic information for satellite measurements of SST, salinity, and absolute SSH, but its spatial coverage is coarser than that of Argo (roughly one float on a 5°x5° grid)
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From page 132... ...
. Because the vehicles provide observations that are ultimately assimilated into ocean models, their optimal control is thus often linked to uncertainty prediction and data assimilation (e.g., Lermusiaux, 2007; Schofield et al., 2010)
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From page 133... ...
Effective integration of the increasing ocean observations and platforms with S2S ocean modeling systems is also necessary, including data-model comparisons for improving ocean model formulations and advanced data assimilation for better S2S forecasts. Finding 5.4: Continued investment into routine space-based observations of sea surface height, sea surface temperature, and surface winds -- which represent key inputs to estimates of air-sea fluxes of water, heat, and momentum -- are critical to support S2S prediction systems.
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From page 134... ...
. Sea ice thickness is less well observed than sea ice concentration, but it is at least as important for sea ice prediction (Blanchard-Wrigglesworth et al., 2011a; Day et al., 2014)
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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)
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From page 136... ...
. As also mentioned in Chapter 4, a number of recent studies have found that more realistic initialization of precipitation and land surface variables, such as soil moisture, snow cover, and vegetation in coupled ESMs and multi-model forecast systems improves the predictability of atmosphere and hydrologic variables on S2S timescales (Koster and Walker, 2015; Koster et al., 2004, 2010, 2011; Kumar et al., 2014; Peings et al., 2011; Prodhomme et al., 2015; Roundy and Wood, 2015; Thomas et al., 2015)
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From page 137... ...
Although in situ networks need to be maintained and in some cases expanded in the near term to enhance S2S forecasting (see below) , measurements from satellites may hold the most promise for improving the global characterization of many land surface variables.
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From page 138... ...
. In addition to improved precipitation, soil moisture, and snow measurements for initializing S2S prediction systems, a number of other land surface measurements are important for advancing S2S model calibration and development and for initializing next generation operational systems.
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From page 139... ...
Finding 5.7: Land observations are critical for modeling large-scale land surfaceatmosphere feedbacks and for making predictions of the terrestrial water cycle. Networks of in situ measurements of precipitation, snow depth, and root-zone soil moisture are likely to remain important, but the poor spatial coverage of such networks currently limits S2S prediction.
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From page 140... ...
Here, various components of the initial condition are perturbed or removed and then forecast skill impacts are assessed. As also mentioned above, a number of recent sensitivity studies have explored the importance of soil moisture initializations and associated feedbacks for S2S predictions (e.g., Fennessy and Shukla, 1999; Guo et al., 2011; Koster et al., 2004)
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From page 141... ...
Using this methodology, a perturbation experiment is then run in which hypothetical observations are evaluated in the context of data assimilation and hypothetical forecasts. Such OSSE experiments have been used in the design and decision phases for the Aeolus Doppler wind lidar instrument for NWP (Baker et al., 2014; Stoffelen et al., 2006)
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From page 142... ...
Relatively robust observing networks exist for the atmosphere over land (outside the polar regions) , but current observations networks for the ocean, cryosphere, and land surface will require more attention in order to advance S2S forecasts over the next decade.
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From page 143... ...
For the cryosphere, continued investment into generating year-round, remotely sensed sea ice thickness measurements, including snow depth on top of the sea ice, are critical, though in situ measurements may continue to be needed in order to translate these measurements into dependable and timely routine estimates. For the land surface, new and/or planned missions for surface soil moisture, surface water, and evapotranspiration may add considerable value to S2S forecasting, especially for model development, but again many of these are research missions with limited lifespans.
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From page 144... ...
Cost-benefit analyses are necessary to justify the financial and logistical burden. Recommendation E: Maintain continuity of critical observations, and expand the temporal and spatial coverage of in situ and remotely sensed observations for Earth system variables that are beneficial for operational S2S prediction and for discovering and modeling new sources of S2S predictability.
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From page 145... ...
• Expand in situ measurements of precipitation, snow depth, soil moisture, and land surface fluxes, and improve and/or better exploit remotely sensed soil moisture, snow water equivalent, and evapotranspiration measurements. • Continue to invest in observations (both in situ and remotely sensed)
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From page 146... ...
A key purpose of DA is to create initial conditions, which are used to produce operational forecasts as well as retrospective forecasts and reanalysis (see Figure 5.1)
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climate prediction, improving the assimilation of atmospheric observations has yielded significant gains in numerical weather prediction skill (Figure 5.6)
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From page 148... ...
Such scalability has implications for coupled DA (see below) given that strong coupling can be achieved without the need for the adjoint of the coupled models (Bishop and Martin, 2012)
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From page 149... ...
to be useful for DA directly, and SWE has not been available with sufficient accuracy or coverage. Often, land surface state estimates are generated offline using Land Data Assimilation Systems (LDAS)
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From page 150... ...
Other Earth system components are also moving toward ensemble Kalman filter-based or hybrid data assimilation algorithms, allowing for the possibility of seamless assimilation and/or synergy within a framework of coupled data assimilation. Finding 5.10: Research activities in data assimilation schemes are occurring uniformly across fields, including for land, ocean, and ice applications, but also for engineering, applied mathematics, and other sciences.
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has already developed a weakly coupled system for both reanalysis (necessary for retrospective forecasts and calibration) and the generation of initial conditions for the real-time operational seasonal forecasts (Saha et al., 2010)
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From page 152... ...
How can the error characteristics of the multiple scales and dynamics be efficiently represented for FIGURE 5.7 Schematic diagram illustrating 4D-Var data assimilation. NOTES: Over the period of the assimilation window, 4D-Var is performed to assimilate the most recent observations (obs, marked as blue stars)
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. The first set of experiments assimilated only atmospheric observations into the coupled model using weakly and strongly coupled assimilation configurations.
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In conclusion, to be successful, strongly coupled DA for S2S systems requires research in efficient methods, multiscale and coupled-dynamics assimilation updates, non-Gaussian nonlinear updates, and reduced-order stochastic schemes for efficient forecasting of coupled statistics.
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Bayesian Data Assimilation, Reduced-order Uncertainty Quantification and Probabilistic Forecasting As mentioned above, many DA algorithms used in operational systems today are linear, based on linearizations, or based on varied heuristic hypotheses and ad hoc approximations. Most of these assumptions are related to the probability densities of the model state and its errors, and of the observations and their errors.
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From page 156... ...
is that they become equivalent to Gaussian schemes when a single component is found sufficient to describe the forecast pdfs but can represent more complex multi-modal pdfs by increasing and optimizing the number of components in the mixture. Such Bayesian DA methods could be further developed for the S2S system components and for strongly coupled DA.
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From page 157... ...
These methods, which allow for the optimal use of the full probabilistic information and utilize rigorous reduced-order differential equations, hold promise for integrating components of S2S prediction systems and for coupled data assimilation. Needs include research on hybrid methods, multiscale and coupled-dynamics assimilation updates, Bayesian data assimilation, and rigorous reduced-order stochastic methods.
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From page 158... ...
Having physically consistent data sets across Earth system components is vital for diag osing, initializing, and validating S2S prediction systems, which are dependent on n representing coupling in order to realize forecast skill. Despite their importance, global r eanalyses are a huge undertaking, requiring a massive amount of staff time, computation time, and data.
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From page 159... ...
Improving the temporal continuity and the frequency of reanalysis may be particularly beneficial. Data Assimilation to Improve Cloud Representation One difficult problem in atmospheric DA involves the use of cloudy and precipitationaffected satellite radiances (Bauer et al., 2011; Errico et al., 2007)
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From page 160... ...
Finding 5.15: In addition to being useful for optimal state estimation, data assimilation can be an extremely powerful tool for performing parameter estimation and optimizing model performance, which may become critical for S2S applications. It is important that reanalysis data sets and diagnostics therein, such as analysis increment and innovation statistics, continue to be publicly disseminated to assist in parameterization development and parameter estimation.
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The potential of the first strongly coupled algorithms has already recently been demonstrated for simple coupled models with 4DVar (Smith et al., 2015a) and EnKF schemes (e.g., Sluka et al., 2015)
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As a whole, novel parameter estimation and model learning schemes are promising and critical for S2S applications. Recommendation G: Invest in research that advances the development of strongly coupled data assimilation and quantifies the impact of such advances on operational S2S forecast systems.
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From page 163... ...
We first discuss in general terms model errors and the steps that need to be taken to reduce them. For convenience, issues more specific to advancing models of the atmosphere, ocean, land surface, and sea ice are discussed in separate subsections, although, of course, the full problem is inherently a coupled one.
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From page 164... ...
Figure 5.9 shows an example of the growth of SST errors in coupled model simulations. It is clear that many features of climate model errors are seen in forecasts of only a few weeks or even days in length (e.g., cold quatorial e P acific, generally warm Indian Ocean, and cold Arabian Sea)
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of HadGEM3-AO climate coupled model is shown in the bottom panel. SOURCE: Brown et al.
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From page 166... ...
Atmospheric Models Several steps are essential to improve the atmospheric component of S2S forecast systems. These include increasing model resolution to explicitly represent important atmospheric processes, improving parameterizations of processes that remain unresolved, improving the representation of tropical convection, and enabling global cloud-permitting models.
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From page 167... ...
Improving Parameterizations There are very significant uncertainties with parameterizations of many physical processes in the atmosphere that are not currently resolved by models (e.g., boundary layer, convection, clouds and microphysics, radiation, surface fluxes, land surface and watershed scale processes, gravity wave drag)
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. Improving parameterization requires advanced understanding of the physical processes at play, which involves research on theory, targeted field observations, and the use of cloud-resolving or cloud-permitting models as tools.
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From page 169... ...
The primary barriers are incomplete understanding of real physical processes and the challenges associated with encapsulating new knowledge of how the real atmosphere works in multiple and interacting model parameterizations. Improving the Representation of Tropical Convection There are important challenges associated with almost all aspects of atmospheric model physics, as described above.
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Without deep convection parameterization, deficiencies in schemes for those processes would still lead to model errors (which might be different from 170
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From page 171... ...
Such research will show the way for operational developments beyond the 10-year horizon and could yield significant insights and improvements to operational models with parameterized convection within 10 years. For example, predictability studies with cloud-permitting models might give different indications from coarse-resolution models of what improvements in S2S forecast skill could be possible.
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From page 172... ...
Broadly speaking, many of the issues are similar to those outlined above in the atmospheric models section, including the importance of improving parameterizations of subgrid processes, while also exploring the benefits and trade-offs of increasing ocean model resolutions. Established Ocean Models Most existing ocean models are community models and are used extensively for a wide range of global, basin-scale, and regional simulations, with timescales anging r from hours to millennia for both operational forecasting (see GODAE, 2015, for a review)
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From page 173... ...
Finding 5.21: Horizontally structured–mesh ocean models will continue to be the basis for most operational coupled S2S forecasting systems for the foreseeable future. Ocean Eddies, Model Resolution, and Subgrid Processes The current resolution of horizontally structured–mesh ocean models ranges from coarser-mesh, non-eddying (e.g., below the resolution required to resolve large-scale eddies)
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From page 174... ...
The large meanders and rings are readily captured in ocean models with resolutions on the order of 1/4° or finer, while submesoscale eddies are characterized by spatial scales of less than a kilometer and need to be parameterized in essentially all large-scale ocean models (Fox-Kemper et al., 2011)
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From page 175... ...
20 -- for example, groups of scientists who have worked 20 http://www.usclivar.org/climate-process-teams, accessed January 27, 2016.
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New techniques that use different equations depending on the space and timescales are also very promising and would allow for the explicit representation of, for example, small rivers, surface waves, internal waves/tides, nonhydrostatic effects, ecosystem structure, localized hypoxia, or leads in sea ice. All of these approaches are areas of active research in the multiscale ocean modeling community, but for the most part they are not yet ready for immediate deployment in operational S2S forecasting systems.
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From page 177... ...
Most ocean models do not have the vertical resolution to take these effects into account and therefore must be parameterized for an accurate representation of these effects. In particular, Langmuir turbulence can reach the base of the mixed layer and drive entrainment (Harcourt, 2015; Li et al., 1995)
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From page 178... ...
The resulting higher-order accuracy and enhanced refinement capabilities can reduce numerical errors in ocean models, which is a promising development for the longer-term prediction needs of S2S applications. In summary, priorities for ocean model improvements for S2S forecasting include both fundamental numerical capabilities and improved depictions of important oceanic phenomena.
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From page 179... ...
Sea ice models need to capture the physical processes that give rise to the high degree of heterogeneity in sea ice thickness, melt-pond coverage, and other characteristics that influence shortwave radiation, clouds, atmospheric stability, and ocean freshwater exchange. Many NWP models do not include interactive sea ice components, and the local sea ice concentration and thickness in these models is prescribed and constant with only the surface temperature and (sometimes)
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From page 180... ...
Land Surface and Biogeochemical Models The land surface model (LSM) accounts for the land-atmosphere interactive processes, such as the exchange of heat, moisture, and momentum at the surface.
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From page 181... ...
Many more complex hydrologic modeling systems exist with river routing and drainage basin models layered atop land surface models, but they are typically run "off line" and are driven by climate and weather forecasts from coupled models. These models incorporate the influence of human water management and use on surface and groundwater storage and river streamflow.
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From page 182... ...
Land surface models used for S2S prediction also need to improve treatment of the hydrological cycle and aspects of the land surface that are coupled to hydrology. Effort is needed to incorporate surface and underground water storage and river routing in models, including the role of human water management and use.
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From page 183... ...
For S2S prediction, it can be desirable to predict, as opposed to prescribe, certain parts of biogeochemical cycles that influence predictability and/ or involve societally relevant impacts. A volcanic eruption is an example of a biogeochemically relevant event that influences both chemical and physical systems, via the formation of volcanically derived aerosols, which in turn force an atmospheric and/or ecosystem response.
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From page 184... ...
Meanwhile, model errors are also passed between different components, and this error growth represents a consequential limitation to S2S prediction skill. The importance of air-sea coupling, land-air coupling, and sea ice coupling has been fully recognized, but representations of such coupling in models still need substantial improvement.
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From page 185... ...
Precipitation arriving at a catchment basin and delivered to the coastal ocean via an estuary through river routing requires hydrological and land surface models that are coupled to atmospheric and ocean models. Other examples include biogeochemistry models coupled with ocean and land and atmospheric models that include biogeochemical feedback on S2S timescales.
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From page 186... ...
Space-based data provide global and routine coverage, augmenting the limits of temporal and spatial coverage inherent in field observations. Products with reliable vertical profiling of the atmosphere, information of ocean and land surfaces, and higher sampling rates by multiple sensors are the best complements to in situ observations for process studies.
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From page 187... ...
New observing technology, both remote sensing and in situ, and international collaboration/coordination could enhance the ability to meet the demand for more detailed information on interactive and coupled processes within S2S models. Transforming Understanding of Physical Processes to Model Improvement Field observations provide the foundation for new knowledge of interactive processes key to S2S prediction.
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From page 188... ...
As discussed earlier, global and large-domain cloud-permitting and non-hydrostatic ocean models that are well calibrated and validated by field observations serve as vital tools for transforming knowledge gained from field observations to model improvement. Large-eddy simulation (LES)
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From page 189... ...
Thus S2S forecasting serves as both a motivation and an ideal testbed for seamless prediction systems. In operational settings, seamless prediction has the potential to reduce labor costs of maintaining several models to produce operational forecasts with various lead times, and the practice and benefit of seamless prediction systems have been demonstrated by several successful efforts (Brown et al., 2012; Hazeleger et al., 2010; Vitart et al., 2008)
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From page 190... ...
Developing these computational schemes jointly with new uncertainty quantification and DA capabilities would be an efficient path forward: It would directly integrate three critical components of S2S forecasting systems from the start. Several crucial steps need to be taken in parallel to reduce model errors.
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From page 191... ...
They are also essential for model validation and the identification of bias/error sources in coupled ESMs. Spatial coverage of this type of observation (e.g., tropical mooring arrays, land surface flux towers)
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From page 192... ...
parameteriza tion schemes in a holistic manner. • Continue to investigate the potential for reducing model errors through increases in horizontal and vertical resolutions in the atmosphere and other model components, ideally in a coupled model framework (see also Recom mendation L)
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From page 193... ...
Thus improving model representation of land surface and terrestrial hydrology, ocean, sea ice, and upper atmosphere -- including fluxes and feedbacks between these components and the troposphere -- should be central to the S2S research agenda. For example, improving the representation of land surface processes such as soil moisture storage and snow may be important for predicting events such as heat waves, cold surges, storm formation, and predicting runoff may help to enable S2S forecasts of flooding and lake and coastal hypoxia.
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From page 194... ...
Recommendation I: Pursue next-generation ocean, sea ice, wave, biogeochemistry, and land surface/hydrologic as well as atmospheric model capability in fully coupled Earth system models used in S2S forecast systems. Specifically: • Build a robust research program to explore potential benefits (to S2S predic tive skill and to forecast users)
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From page 195... ...
Given the range of possible methods and options for improving forecast skill through such techniques, this section also discusses the optimization of forecast systems through an exploration of costs and benefits of various forecast system configurations. Accounting for Uncertainty to Improve Probabilistic S2S Forecast Reliability and Skill As briefly discussed in Chapter 2, a notable strategy for advancing the skill and utility of S2S forecasts in the past few decades, apart from efforts to reduce model errors, has been the inclusion of quantitative information regarding uncertainty (i.e., probabilistic prediction)
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From page 196... ...
. Different model configurations, along with different parameterizations and physics likely both play a role in this reduction of error: forecast models in different operational centers and institutions have different configurations (e.g., resolutions, physics parameterization schemes, strategies for initialization, ensemble, coupling, and retrospective forecasts)
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From page 197... ...
Calibration of S2S Probability Forecasts Calibration is a post-process that uses statistical methods based on discrepancies between past forecasts and observations to adjust ensemble forecasts and improve forecast skill. Today, all operational S2S models include a number of ensemble members whose individual forecasts can be arranged to estimate probability distributions for the predicted variables, point by point across the forecast grid.
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From page 198... ...
For example, as mentioned earlier in this chapter (Figure 5.7) , SST rrors in coupled model simulations with the UK Met Office model grow rapidly.
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From page 199... ...
. Although such metrics have been used for decades, they provide only a limited view of forecast skill.
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From page 200... ...
Feature-based verification has the advantage that it can "recognize" and verify a feature that may, for example, occur slightly earlier or later, cover a smaller or larger area, be more or less intense, or be of shorter or longer duration, than predicted. This enables more accurate quantitative evaluation of model performance in "near miss" situations and better refinement of model skill and reliability.
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From page 201... ...
There is also a need to develop common S2S-specific forecast skill metrics to target core physical characteristics of the forecast that are particularly relevant to S2S processes and timescales (e.g., SST, sea ice thickness, upper-level atmospheric flow, soil moisture, upper ocean heat content, in addition to indices of S2S relevant modes of variability)
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From page 202... ...
This is particularly true for the land surface (e.g., soil moisture and snow)
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From page 203... ...
This is true for most of the current operational subseasonal forecast systems. Finding 5.38: Retrospective forecasts using the current version of the forecast system and up- to-date reanalyses are important for advancing calibration and validation efforts of ensemble prediction.
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From page 204... ...
. In addition to research on reducing model errors through parameterizations, increases in model resolution, and adding complexity in coupled submodels, it would be enormously beneficial to ascertain which configurations can produce optimum forecast systems, as defined by reliable probability forecasts across a wide spectrum of climate variability and Earth system variables and by optimum levels of user-focused skill.
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From page 205... ...
Recommendation K: Explore systematically the impact of various S2S forecast system design elements on S2S forecast skill. This includes examining the value of model diversity, as well as the impact of various selections and combinations of model resolution, number of ensemble perturbations, length of lead, averaging period, length of retrospective forecasts, and options for coupled sub-models.
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