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Assessment of Interseasonal to Interannual Climate Prediction and Predictability 3 Building Blocks of Intraseasonal to Interannual Forecasting An ISI forecast is made utilizing observations of the climate system, statistical and/or dynamical models, data assimilation schemes, and in some cases the subjective intervention of the forecaster (see Box 3.1). Improvements in each of these components, or in how one component relates to another (e.g., data assimilation schemes expanded to include new sets of observations; observations made as part of a process study to validate or improve parameters in a dynamical model), can lead to increases in forecast quality. This portion of the report discusses these components of ISI forecasting systems, with an emphasis on assessing quality among forecast systems following a change in forecast inputs. Past advances that have contributed to improvements in forecast quality are noted, and the section ends by presenting areas in which further improvement could be realized. HISTORICAL PERSPECTIVE FOR INTRASEASONAL TO INTERANNUAL FORECASTING Scientific weather prediction originated in the 1930s, with the objective of extending forecasts as far into the future as possible. Studies at MIT under Carl Gustaf Rossby consequently included longer time scales than just the daily prediction issue. Jerome Namias became a protégé of Rossby, and took on the task of extending to longer scales as director of the “Extended Forecast Section” of the Weather Bureau/National Weather Service. The approaches developed emphasized upper level pressure patterns that could persist or move according to the Rossby barotropic model, and could provide “teleconnections” from one region to another. These patterns were then used to infer surface temperature and precipitation patterns. The latter were initially done by subjective methods, but soon statistical approaches were adopted through the work of Klein. For more than a few days in advance, prediction of daily weather would necessarily have low skill and so monthly or longer forecasts were obtained as averages. Work by Lorenz in the 1960s explained the lack of atmospheric predictability after more than about 10 days in terms of the chaotic nature of the underlying dynamics (see Chapter 2). At about the same time, Namias was emphasizing the need to consider underlying anomalous boundary conditions as provided by SSTs, soil moisture, and snow cover. The importance of changing tropical SSTs through ENSO was first identified by Bjerknes in the late 1960s. A first 90-day seasonal outlook was released by NOAA in 1974.
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability BOX 3.1 TERMINOLOGY FOR FORECAST SYSTEMS Observation—measurement of a climate variable (e.g., temperature, wind speed). Observations are made in situ or remotely. Many remote observations are made from satellite-based instruments. Statistical model—a model that has been mathematically fitted to observations of the climate system using random variables and their probability density functions. Dynamical or Numerical model—a model that is based, primarily, on physical equations of motion, energy conservation, and equation(s) of state. Such models start from some initial state and evolve in time by updating the system according to physical equations. Data assimilation—the process of combining predictions of the system with observations to obtain a best estimate of the state of the system. This state, known as an “analysis”, is used as initial conditions in the next numerical prediction of the system. Operational forecasting—the process of issuing forecasts in real time, prior to the target period, on a fixed, regular schedule by a national meteorological and/or hydrological service. Initial conditions/Initialization—Initial conditions are estimations of the state (usually based on observational estimates and/or data assimilation systems) that are used to start or initialize a forecast system. Initialization can include additional modification of the initial conditions to best suit the particular forecast system. Progress since the 1960s can be discussed in terms of advances in forecasting approaches (including their evaluation) and improved understanding and treatment of underlying mechanisms. One major direction of advancement in forecasting has been that of dynamical modeling (see “Dynamical Models”section in this chapter). Generally the dynamical models continued to improve according to advancements in computational resources and a growing knowledge of the key processes to be modeled. However, official forecasts in the United States depended on subjective interpretation of these objective products. In addition, various statistical (empirical) modeling approaches were developed and improved to remain as capable as the dynamical approaches in their validation. Other countries have been developing similar capabilities for seasonal prediction since the 1980s, largely depending on numerical modeling. Recognition of the role of tropical SST anomalies, especially those associated with ENSO, in driving remote climate anomalies has led to much work in predicting tropical SST. Some of the key advancements in estimating these SSTs developed during the TOGA international study in conjunction with the deployment of the Tropical Atmosphere Ocean (TAO) array in the 1980s and 1990s (NRC, 1996; see “Ocean Observations” section in this chapter and “ENSO” section in Chapter 4). Further expansion of the efforts in ISI forecasting have been undertaken by CLIVAR (Climate Variability and Predictability), a research program administered by the World Climate
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability Research Programme (WCRP). CLIVAR supports a variety of research programs9 around the world focused on cross-cutting technical and scientific challenges related to climate variability and prediction on a wide range of time scales. CLIVAR also helps to coordinate regional, process-oriented studies (WCRP, 2010). What follows is a description of the “building blocks” of an ISI forecasting system: observations, statistical and numerical models, data assimilation schemes. The quality and use of forecasts are also discussed. It is a broad overview, offering some historical context, an evaluation of strengths and weaknesses, and potential avenues for improvement. At the conclusion of Chapter 3, the key potential improvements are summarized; the Recommendations (Chapter 6) have been made with these improvements in mind. OBSERVATIONS Observations are an essential starting point for climate prediction. In contrast to weather prediction, which focuses primarily on atmospheric observations, ISI prediction requires information about the atmosphere, ocean, land surface, and cryosphere. Also in contrast to weather prediction, the observational basis for ISI prediction is both less mature and less certain to persist. Indeed, both continuing evolution and the need to sustain observations for ISI prediction are seen as issues at present and into the future. International cooperation and the governance of the World Meteorological Organization do much to ensure continuity of weather observations. Similar international cooperation is being developed for climate observations, but formal international commitments to these observations are not the general case. The following sections describe some of the platforms available for making these observations, and the increase in the number of observations over time. Observations of quantities that end-users track, such as sea surface temperature and precipitation, and of quantities that record the coupling between elements of the climate system, such as soil moisture and air-sea fluxes, are particularly useful to assess both the realism of models and identify longer-term variability and trends that provide the context for ISI variability. However, current observational systems do not meet all ISI prediction needs, or are not always used to maximum benefit by ISI prediction systems. Some observations for the Earth system needed for initialization are not being taken, or are not available at a spatial or temporal resolution to make them useful. Some observations have not been available for a sufficiently long period of time to permit experimentation, validation, verification, and inclusion within statistical or dynamical models. In yet other cases, the observations are available, but they are not being included in data assimilation schemes. Additionally, regionally enhanced observations or studies that target learning more about the processes that govern ISI dynamics, including developing improved parameterizations of processes that are sub-grid scale in dynamical models, are needed. New observations, both in situ and remotely sensed, may be available through research programs. Part of the challenge is to integrate these new observations, assess their utility and impact, and then, if the observations contribute to ISI prediction, develop the advocacy required to sustain them. Integration of observational efforts, as in CLIVAR climate process teams or by 9 Programmatic evaluation of the U.S. CLIVAR project office can be found in NRC (2004). Historical strategic recommendations germane to ISI forecasting for CLIVAR’s Global Ocean-Atmospehre-Land program (GOALS) can be found in NRC (1998).
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability bringing together observationalists with operational centers to engage in observing system simulation studies and assessments of the improvement stemming from observations have merit. Heterogeneous networks of observations, at times obtained by different organizations, may need better integration into accessible data bases and particular attention from partnered observationalists and modelers. For all observations, appropriate attention to metadata and data quality, including realistic estimates of uncertainty, are essential to ensuring their use and utility. Atmosphere Over the years the conventional meteorological observing system evolved from about 1,000 daily surface pressure observations in 1900 to about 100,000 at the present. Likewise, upper air observations (rawinsondes, pilot balloons, etc.) grew from less than 50 soundings in the 1920s to about 1,000 in the 1950s (of which most were pilot balloons). Today, there are about 1,000 rawinsondes used regularly (Dick Dee, personal communication). Satellite observations, introduced into operations in 1979, ushered in a totally new era of numerical weather prediction, although it was only in the 1990’s that the science of data assimilation (see “Data Assimilation” section in this chapter) progressed enough to demonstrate that there was a clear positive impact from satellite data when added to rawinsondes in the Northern Hemisphere. Figure 3.1 illustrates the huge increase of different types of available satellite observations in the last two decades assimilated for ECMWF operational forecasts. These satellite products have not only grown in number, but also in diversity. They can provide information about atmospheric composition and hydrometeors, as well as vertical profiles of thermodynamic properties. The assimilation of each of these observing systems poses a new challenge, and the full impact of each may not become clear for years because of the partial duplication of information among the different systems. It is often difficult to attribute an increase in prediction quality to the incorporation of a new set of observations in an ISI forecasting system. Some examples of improvements arising from the assimilation of specific observations, such as AMSU radiances, are discussed in the “Data Assimilation” section of this chapter. The incorporation of targeted observations that focus on atmospheric processes that are sources of ISI predictability could also contribute to ISI forecast quality. In some cases, these observations exist for research purposes but are not being exploited by ISI forecast systems. In other cases, these observations do not exist. For example, high resolution observations of the vertical structure of the tropical atmosphere could improve the understanding of the MJO, the ability to validate current dynamical models, and perhaps the parameterization of these models. This is part of the mission of the Dynamics of the MJO experiment (DYNAMO; http://www.eol.ucar.edu/projects/dynamo/documents/WP_latest.pdf). Oceans As mentioned in Chapter 2, the oceans are a major source of predictability at intraseasonal to interannual timescales. The ocean provides a boundary for the atmosphere where heat, freshwater, momentum, and chemical constituents are exchanged. Large heat losses and evaporation at the sea surface cause convection and make surface water sink into the interior,
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability FIGURE 3.1: Number of satellite observing systems available since 1989 and assimilated into the ECMWF system. Each color represents a different source/platform. SOURCE: Courtesy of Jean-Noel Thepaut, ECMWF. while surface heating and the addition of freshwater make surface water buoyant and resistant to mixing with deeper water. Variability in the air-sea fluxes, in oceanic currents and their transports, and in large-scale propagating oceanic Rossby or Kelvin waves all contribute to the dynamics of the upper ocean and the sea surface temperature. In turn, the states of the ocean surface and sub-surface can force the atmosphere on intraseasonal to interannual timescales, as is clearly evident in the ENSO and MJO phenomena. Therefore, the initialization of sea surface and sub-surface ocean state is required for near-term climate prediction. Unfortunately, the comprehensive observation of the global oceans started much later than in the atmosphere and even today there are challenges that prevent collection of routine observations over large parts of the ocean. The significant climatic impacts of ENSO, especially after the 1982–1983 event, demonstrated that a sustained, systematic, and comprehensive set of observations over the equatorial Pacific basin was needed. The TAO/Triangle Trans-Ocean Buoy Network (TRITON) array was developed during the 1985–1994 Tropical Ocean Global Atmosphere (TOGA) program (Hayes et al., 1991, McPhaden et al., 1998). The array spans one-third of the circumference of the globe at the equator and consists of 67 surface moorings plus five subsurface moorings. It was fully in place to capture the evolution of the 1997–1998 El Niño. In 2000, the original set called TAO was renamed TAO/TRITON with the introduction of the TRITON moorings at 12 locations in the western Pacific (McPhaden et al., 2001). TAO/TRITON has been the dominant source of upper ocean temperature and in situ surface wind data near the equator in the Pacific over the past 25 years and has provided the observational underpinning for theoretical explanations of ENSO such as the recharged oscillator
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability (e.g. Jin, 1997). It provides a key constraint on initial conditions for seasonal forecasting at many centers around the world. After the success of the TAO/TRITON array, further moored buoy observing systems have been developed over the Atlantic (PIRATA) and Indian (RAMA) oceans under the Global Tropical Moored Buoy Array (GTMBA) program. The moorings allow simultaneous observations of surface meteorology, the air-sea exchanges of heat, freshwater, and momentum, and the vertical structure of temperature, salinity, horizontal velocity, and other variables in the water column. Thus, they provide the means to monitor both the air-sea exchanges and the storage capacity of the upper ocean. The PIRATA array was designed for the purpose of improving the understanding of ocean-atmosphere interactions that affect the regional patterns of climate variability in the tropical Atlantic basin (Servain et al., 1998). The array, launched in 1997 and still being extended, currently has 17 permanent sites. The RAMA array was initiated in 2004 with the aim of improving our understanding of the east Africa, Asian, and Australian monsoon systems (McPhaden et al., 2009). It currently consists of 46 moorings spanning the width of the Indian Ocean between 15ºN and 26ºS. It is expected to be fully completed in 2012. The maintenance of the GTMBA is absolutely essential for supporting climate forecasting. However, there are many difficulties in maintaining these arrays, not the least of which is identifying institutional arrangements that can sustain the cost of these observing systems (McPhaden et al., 2010). Away from the equator, the permanent in situ moored arrays are sparser and address sample the characteristic extra-tropical regions of the ocean-atmosphere system under the international OceanSITES program. Few such sites exist in high latitude locations, but efforts are underway in the United States (under the National Science Foundation Ocean Observatories Initiative) and in other countries to add sustained high latitude ocean observing capability. In parallel to the development of the moored buoy arrays, the observation of SST has improved markedly over the last 20 years. SST is a fundamental variable for understanding the complex interactions between atmosphere and ocean. Since 1981, operational streams of satellite SST measurements have been put together with in situ measurements to form the modern SST observing systems (Donlon et al., 2009). Since 1999 more than 30 satellite missions capable of measuring SST in a variety of orbits (polar, low inclination, and geostationary) have been launched with infrared or passive microwave retrieval capabilities. New approaches to integrate remote sensing observations with in situ SST observations that help reduce bias errors are being taken (Zhang et al., 2009). Despite the evident progress, an important issue remains: satellite observations of SST only started in the 1980s and satellites have a relatively short life span. Therefore, further work is necessary to ensure the “climate quality” of the data over long periods. This would facilitate the generation of SST re-analysis products for operational seasonal forecasting (Donlon et al., 2009). Even with the evident progress made with the tropical moored buoy arrays and the improvement of the satellite measurements of SST, as recently as the late 1990s there were still vast gaps in observations of the subsurface ocean. Such observations are needed for seasonal to interannual prediction. The ability of the ocean to provide heat to the atmosphere, the extent to which the upper ocean can be perturbed by the surface forcing, and the dynamics of the ocean that lead to changes in the distribution of heat and freshwater all depend on the vertical and horizontal structure of the ocean and its currents. Surface height observations by satellite altimeters have added information about the density field in the ocean and thus, for example, the redistribution of water properties and mass
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability along the equator associated with ENSO. Efforts to quantify the state of the ocean were further improved by the international implementation of the Argo profiling float program (http://www.argo.ucsd.edu/). Until then, most sub-surface ocean measurements were taken by expendable bathythermograph (XBT) probes measuring temperature versus depth and by shipboard profiling of salinity and temperature versus depth from research vessels, which are both limited in their global spatial coverage and depth. The Argo program was initiated in 1999 with the aim of deploying a large array of profiling floats measuring temperature and salinity to 1,500 to 2,000 meters deep and reporting in real time every 10 days. To achieve a 3º x 3º global spacing, 3,300 floats were required between 60ºS and 60ºN. As of February 2009, there are 3,325 active floats in the Argo array. After excluding floats from which data was not passing the quality control and those in high latitudes (beyond 60º latitude) or in heavily sampled marginal seas, the number of floats is only 2,600. Argo data is distributed via the internet without restriction and about 90% of the profiles are available within 24 hours of acquisition. Quality control continues after receipt of the data, particularly for the salinity observations. To improve the quality of data from Argo floats, ship-based hydrographic surveys obtaining salinity and temperature profiles are needed, and the process may require several years before the Argo data experts are confident that the best data quality has been achieved (Freeland et al., 2009). However, the real time Argo data is a critical contribution. With it, the depth of the surface mixed layer can be mapped globally, thus determining the magnitude (depth and temperature) of the oceanic thermal reservoir in immediate contact with the atmosphere. Internationally, there is a coordinated effort under the Joint Commission on Oceanography and Marine Meteorology (JCOMM) of the World Meteorological Organization (WMO) and the International Oceanographic Commission (IOC) to coordinate sustained global ocean in situ observations, including Argo floats, surface SST drifters, Volunteer Observing Ship-based measurements, tropical moored arrays, and the extra-tropical moored buoys. Remote observations of surface vector winds combined with drifting buoy data can be used to identify the wind-driven flow of the upper ocean, thus complementing the ability of Argo floats and altimetry to observe the density-driven flow. Future satellite observations of interest include those of surface salinity. The in situ ocean observing community will benefit from an ongoing dialog with those interested in improving prediction on intraseasonal and interannual timescales. Programs such as the World Climate Research Programme’s (WCRP) CLIVAR work to coordinate sustained observations in the ocean with focused process studies that improve understanding of climate phenomena and processes. Distributed and sustained ocean and air-sea heat flux observations with global and full depth coverage are being used to identify biases and errors in coupled and ocean models. These include the surface buoys and associated moorings of OceanSITES and the repeat hydrographic survey lines done in each basin every 5–10 years. The moorings provide high temporal resolution sampling from the air-sea interface to the seafloor, while the surveys map ocean properties along basin-wide sections. Both programs provide data sets that quantify the structure and variability of the ocean that are often found in model fields. In contrast, denser sampling arrays are deployed for a limited duration as part of process studies. These studies are
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability FIGURE 3.2 Examples of the spatial distribution of various ocean observations mentioned in the text. Top panel: Argo floats, which can provide surface and sub-surface information. SOURCE: Argo website (http://www.argo.ucsd.edu/) Middle panel: Drifters, which can provide SST, SLP, wind, and salinity information (see colors in legend). SOURCE: NOAA (http://www.aoml.noaa.gov/phod/dac/gdp.html). Bottom panel: OceanSITES, intended for long-term observations for depths up to 5000m in a stationary location. SOURCE: (OceanSITES http://www.jcommops.org/FTPRoot/OceanSITES/maps/200908_VISION.pdf)
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability designed to improve our understanding of physical processes and to aid in the parameterization of the processes not fully resolved by models. CLIVAR also works to build connectivity among the observing community, researchers investigating ocean processes and dynamics, and climate modelers. Process studies by CLIVAR and others add to understanding of ocean dynamics, develop improved parameterizations of processes not resolved in ocean models, and guide longer term investments in ocean observing. Land The land variables of potential relevance for seasonal prediction—the variables for which accurate initialization may prove fruitful—are soil moisture, snow, vegetation structure, water table depth, and land heat content. These variables help determine fluxes of heat and moisture between the land and the atmosphere on large scales and thus may contribute to ISI forecasts. In addition, some of these variables are associated with local hydrology and hydrological prediction (e.g., observations of snow in a mountain watershed in the winter can provide information on spring water supply). This evolution in the use of land and hydrological observations mirrors the emerging interest in new types of ocean observations, noted in the previous section. Despite their importance to the surface energy and moisture balances and fluxes, our ability to measure such land variables on a global scale is extremely limited. Thus, alternative approaches for their global estimation have been, or still have to be, developed. Soil Moisture Of the listed land variables, soil moisture (perhaps along with snow) is probably the most important for subseasonal to seasonal prediction. For the prediction problem, however, direct measurements of soil moisture are limited in three important ways. First, each in situ soil moisture measurement is a highly localized measurement and is not representative of the mean soil moisture across the spatial scale considered by a model used for seasonal forecasting. Second, even if a local measurement was representative of a model’s spatial grid scale, the global coverage of existing measurement sites would constitute only a small fraction of the Earth’s land area, with most sites limited to parts of Asia and small regions in North America. Finally, even if the spatial coverage were suddenly made complete, the temporal coverage would still be lacking; long historical time series (decadal or longer) may be needed to interpret a measurement properly before using it in a model. Satellite retrievals offer the promise of global soil moisture data at non-local scales. Data from the Scanning Multichannel Microwave Radiometer (SMMR) and Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) instruments, for example, have been processed into global soil moisture fields (Owe et al., 2001; Njoku et al., 2003). Figure 3.3 shows an example of the mean soil moisture as observed by the SMMR instrument. Such instruments, however, can only capture soil moisture information in the top few millimeters of soil, whereas the soil moisture of relevance for seasonal prediction extends much deeper, through the root zone (perhaps a meter). The usefulness of satellite soil moisture retrievals or their associated raw radiances will likely increase in the future as L-Band measurements come online
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability FIGURE 3.3 Mean soil moisture (m3/m3) in upper several millimeters of soil, as estimated via satellite with the SMMR instrument using the Owe et al. (2001) algorithm. SOURCE: Adapted from Reichle et al. (2007). and data assimilation methods are further developed (see section on “Data Assimilation” in this chapter and the soil moisture case study in Chapter 4). Currently, global soil moisture information for model initialization has to be derived indirectly from other sources. A common approach is to utilize the soil moisture produced by the atmospheric analysis already being used to generate the atmospheric initial conditions. This approach has the advantage of convenience, and the soil moisture conditions that are produced reflect reasonable histories of atmospheric forcing, as generated during the analysis integrations—if the analysis says that May is a relatively rainy month, then the June 1 soil moisture conditions produced will be correspondingly wet. The main meteorological driver of soil moisture, however, is precipitation, and analysis-based precipitation estimates are far from perfect. Thus, a more careful approach to using model integrations to generate soil moisture initial conditions has been developed in recent years. This approach is commonly referred to as LDAS, for “Land Data Assimilation System”, although the term is something of a misnomer; true land data assimilation in the context of the land initialization problem is discussed further in the “Data Assimilation” section below. LDAS systems are currently in use for some experimental real-time seasonal forecasts and are planned for imminent use in some official, operational seasonal forecasts. An operational LDAS system produces real-time estimates of soil moisture by forcing a global array of land model elements offline (i.e., disconnected from the host atmospheric model) with real-time observations of meteorological forcing. (Here, real-time may mean several days to a week prior to the start of the forecast, to allow time for processing.) Real-time atmospheric data assimilation systems are the only reasonable global-scale sources for such forcings as wind speed, air temperature, and humidity. However, the evolution of the soil moisture state depends even more on precipitation and net radiation, whose reanalysis estimates are not reliable.
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability Consequently, LDAS systems use alternative sources such as merged satellite-gauge precipitation products (e.g., CMAP, or the Climate Prediction Center Merged Analysis of Precipitation) and satellite-based radiation products (e.g., AFWA AGRMET, or Air Force Weather Agency Agricultural Meteorology Modeling System). The LDAS system may still need atmospheric analysis data for the sub-diurnal time sequencing of the forcing, but the alternative data sources prove invaluable for “correcting” these precipitation and radiation time series so that their temporal-averages are realistic. Such LDAS systems also require global distributions of surface parameters (vegetation type, soil type, etc.), currently available in various forms (e.g., Rodell et al., 2004). Consistency between the parameter set used for the LDAS system and that used for the full forecast system is an important consideration. Snow Real-time direct measurements of snow on the global scale do not exist, though some measurements are available at specific sites, for example, in the western United States (Snowpack Telemetry, SNOTEL) and through coded synoptic measurements made at weather stations (SYNOP). For global data coverage, satellite measurements are promising—certain instruments (e.g., MODIS) can estimate snow cover accurately at high resolution on a global scale. Satellite snow retrievals, however, also show significant limitations. For the seasonal forecasting problem, snow cover is not as important as snow water equivalent (SWE), which is the amount of water that would be produced if the snowpack were completely melted. Satellite estimates of SWE are made difficult by the sensitivity of the retrieved radiances to the morphology (crystalline structure) of the snow, which is almost impossible to estimate a priori—a given snowpack may have numerous vertical layers with different crystalline structures, reflecting the evolution of the snowpack with time through compaction and melt/refreeze processes. Compounding the difficulty of estimating SWE from space are spatial heterogeneities in snowpack associated with topography and vegetation. The LDAS approach described above can provide SWE in addition to soil moisture states, assuming the land model used employs an adequate treatment of snow physics. In the future, the merging of LDAS products with the available in situ snow depth information and satellite-based snow data in the context of true data assimilation (see “Data Assimilation” section) will likely provide the best global snow initialization for operational forecasts. Vegetation Structure Current operational seasonal forecast models treat vegetation as a boundary condition, with prescribed time-invariant vegetation distributions and (often) prescribed seasonal cycles of vegetation phenology, e.g., leaf area index (LAI), greenness fraction, and root distributions. Early forecast systems relied on surface surveys of these quantities, and modern ones generally rely on satellite-based estimates. Reliable dynamic vegetation modules would, for the seasonal prediction problem, allow the initialization and subsequent evolution of phenological prognostic variables such as LAI and rooting structure. A drought stressed region, for example, might be initialized with less leafy
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability FIGURE 3.15 Improvement in forecasts from the latest ECMWF forecast system (red line) compared to earlier versions (blue and green lines). The black line corresponds to a Persistence forecast. Metric shown is RMS error for Nino 3.4 SST for 64 forecasts in the period 1987–2002. SOURCE: ECMWF, Anderson et al (2007). accomplishment. Incremental improvements in the ECMWF dynamical model are illustrated through successive reduction in the RMSE of Nino3.4 predictions (Figure 3.15). Comparative assessments of new versus old forecast systems can really only be quantified for fully objective forecast systems, although one could demonstrate improvements of a newer objective system over a previous non-objective system (i.e., one involving subjective intervention). It should also be noted that these assessments of improvement potentially suffer from sampling issues, since there are typically not more than 20 years of retrospective forecasts for comparison. However, there are coordinated international efforts (e.g., the Climate Historical Forecast Project; CHFP) to extend the retrospective forecast period further back in time. Currently, forecast quality is often difficult to compare across systems because of differences in forecast format, verification data, the choice of skill metrics, or even differences in graphical appearance. A mechanism to provide a consistent view of prediction quality across models was established in 2006 by the World Meteorological Organization. The charge was taken up by the lead center for the Standard Verification System of Long Range Forecasts (LC-LRFMME: http://www.bom.gov.au/wmo/lrfvs/) co-hosted by the National Meteorological Services of Australia and Canada. The LC-SVSLRF responsibilities include maintaining an associated website displaying verification information in a consistent and similar way. It allows forecasting centers to document prediction quality measured according to a common standard. The SVS is defined in Attachment II.8 (p. 122) of the WMO Manual on the Global Data-Processing and Forecasting System (WMO No. 485). Unfortunately, the goal of the comparative assessment envisioned by the WMO has not been achieved because it depends on the cooperation of the global producing centers (http://www.wmolc.org) to contribute consistent verification data, preferably in a common graphical format, which has not yet happened. Comparative estimates of quality can be similarly difficult to quantify, even for the U.S. forecasts. One of the few studies to date compares the official subjective forecasts since 1995 with a newly implemented objective methodology that combines three statistical and one
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability dynamical tool (Figure 3.16; O’Lenic et al., 2008). As stated above, the objective combination outperforms the subjective forecasts. The skill metric used in that study is the Heidke score, which was discussed in the previous section; it is not advocated by the WMO-SVSLRF. Assessment of forecast quality from the NCEP CFS model does not use Heidke skill scores, but rather correlation, Brier skill scores (BSS), and reliability diagrams (Saha et al., 2006). In Saha et al. (2006), a widely used statistical tool is compared to the dynamical model, with the result that the two methods have comparable but complementary BSS; regions of highest skill rarely overlap. Their result strongly suggests that additional predictability that is seen by the statistical tool but not currently captured in the dynamical model could result from improvements to that model. It also suggests the benefit of using both statistical and dynamical modeling approaches for seasonal climate prediction. Combined Forecast Systems A growing body of literature touts the benefit of multiple prediction inputs in climate forecasts. Many national centers that produce real-time forecasts include one or more dynamical models, one or more statistical models, and perhaps also the subjective interpretation or experience of the forecasters involved. As this practice continues, and as more prediction inputs become openly available, it is possible to assess the relative benefits of each type of prediction input to the quality of the forecast. In addition, as more prediction data becomes openly available, new methods for making the best use of that information can be tested and documented. Subjective Combination Since the early 1970s, weather forecasts in the United States and elsewhere were subjectively derived using the objective input as guidance (Glahn, 1984). In the mid-1980s, comparison of the skill of these objective and subjective forecasts according to several metrics indicated that the subjective weather forecasts were generally more skillful than the objective ones for shorter lead times (e.g. 12–24 hours), whereas the two types of forecasts exhibited approximately equal quality for longer lead times (e.g. 36–48 hours; Murphy and Brown, 1984). The same study further showed that both types of forecasts had positive trends in correlation skill over the decade, with improvements in objective forecasts equaling or exceeding improvements in subjective forecasts. The use of subjective guidance has continued to this day for weather and now climate forecasts. Many seasonal-to-interannual forecasting centers, particularly those that use multiple prediction inputs, maintain a subjective element in their forecasts. At CPC and UKMO, for example, inputs from both statistical and dynamical prediction tools are considered and discussed, prior to “creating” a forecast (Graham et al., 2006; O’Lenic et al., 2008). In some instances, a subset of the tools will be objectively combined prior to their consideration next to other tools. Starting around 2006, CPC began objectively combining its main prediction tools, which consist of the Climate Forecast System (CFS) dynamical model and three statistical prediction tools, using an adaptive regression technique. This consolidation serves as a “first guess” but then is discussed with a number of other inputs, which include other consolidations as
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability FIGURE 3.16 Objective forecasts tend to perform better than forecasts with a subjective element. Lines represent the percentage improvement of forecasts relative to climatology for a 3-month forecast issued with a 1/2 –month lead time. The mean skill of objective forecasts for the entire period (solid horizontal line, “CON” for “objective consolidation”) is above the mean skill of the forecasts with a subjective element for the entire period (dashed horizontal line, “OFF” for “official forecasts”). The individual forecasts (non-horizontal lines) throughout the period often indicate a similar relationship. Top panel: temperature; bottom panel: precipitation. SOURCE: Adapted from O’Lenic et al. (2008). well as individual tools and may or may not incorporate historical forecast quality. Comparison of the official forecasts, which include the subjective intervention, against the purely objective consolidation indicates that the subjective element reduces forecast quality (O’Lenic et al., 2008), particularly during winter in the absence of a strong ENSO signal (Livezey and Timofeyeva, 2008).
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability A notable subjective element also exists in regional climate outlook forums (RCOFs14). These forums, initiated in the late 1990s by the WMO, National Meteorological and Hydrological Services (NMHSs) and other international organizations, bring together countries within a region, such as Southeastern South America or the Greater Horn of Africa, to develop a consensus outlook for the climate of the upcoming season. Seasonal climate predictions from the participating NMHSs are discussed in conjunction with those from international centers. The inputs are not combined objectively or systematically, although they do often consider past forecast quality in the discussions. Some analyses suggest that the subjective element of the process causes the forecasts to be quantitatively less skillful than if the input predictions were combined more objectively (Berri et al., 2005). Efforts are underway to produce more consolidated inputs and other objective input tools that can be used in the RCOFs to encourage the reduction of the subjective element. One of these efforts includes the establishment of a Lead Centre of Long-Range Forecast Multi-Model Ensembles (LC-LRFMME15), which objectively combines the predictions contributed by the current nine Global Producing Centres (GPCs). However, associated skill information, which would presumably be provided by the WMO Lead Centre for the Long Range Forecast Verification System (LRFVS16), does not accompany these forecasts primarily because this model performance information is not provided by the GPCs. The GPCs also do not readily provide access to the historical model data that would allow users to evaluate the performance for themselves. In terms of other objective input tools, one that has been increasingly used in the RCOFs is the Climate Predictability Tool17, which allows forecasters to develop statistical predictions that use as input either observed precursors or dynamical model output. Objective Combination of Predictions As discussed in the previous sections, the few studies that have compared objective forecasts and the subjective forecasts to which they contribute indicate that the subjective element degrades the quality of the objective “first guess” (Berri et al., 2005; O’Lenic et al., 2008). But beyond that, objective methods allow a forecaster to demonstrate how the forecast would have performed in the past given new prediction inputs, which is not possible if the inputs are subjectively combined. Statistical and dynamical predictions each have their own merits and should not necessarily be viewed as competitors. It is nonetheless desirable to compare the performance of statistical and dynamical tools when both exist for a given prediction target. This comparison can serve two purposes if there is a clear difference in performance: first, it may indicate that an important process is missing from one of the prediction approaches, and second, it may indicate that one of the predictions be given greater weight in the final forecast. Several studies have shown that statistical and dynamical methods have comparable quantitative skill for specific forecast targets such as ENSO (e.g. Saha et al., 2006) or precipitation in some parts of the world (e.g. Moura and Hastenrath, 2004). In other parts of the world, such as the United States, statistical and dynamical information bring complementary information (e.g. Saha et al., 2006). 14 http://www.wmo.int/pages/themes/climate/consensus_driven_predictions.php 15 http://www.wmolc.org/ 16 http://www.bom.gov.au/wmo/lrfvs/ 17 http://iri.columbia.edu/climate/tools/cpt
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability The same can be said for different statistical predictions, such as those capturing ENSO teleconnections compared to those isolating recent trends (e.g. Livezey and Timofeyeva, 2008). In addition, considerable value can be gained by employing the two approaches together, such as model output statistics (MOS, see “Correlation and Regression” section in this chapter), which refers broadly to the statistical correction of dynamical models. MOS techniques can be used to correct systematic biases of dynamical models by translating the aspects of the observed variability that the model captures correctly into something that more closely resembles the observations (e.g. Feddersen et al., 1999; Landman and Goddard, 2002; Tippett et al., 2005). By far, the greatest boost to objective combination of prediction inputs has come through advances in multi-model ensembles (MME). These advances within the climate community have been particularly rapid since the advent of publically available archives of model data, such as the DEMETER dataset for seasonal-to-interannual predictions (Palmer et al., 2004), with many decades of hindcasts, and the Coupled Model Intercomparison Project v3 (CMIP3) that provided the data of the climate change simulations of the 20th century and projections of the 21st century summarized in the 4th Assessment Report of the IPCC (IPCC, 2007). Although in each case the databases contain coupled ocean-atmosphere models with similar external forcing and/or initial conditions, the dynamical cores of the models and their physical parameterizations differ. The premise holds that although models have deficiencies, they do not all have the same deficiencies. Thus, combining models brings out the robust information they have in common and reduces the individual or random biases that they do not share, which can provide more reliable forecast information. By allowing scientists from all over the world to access a common set of models from different modeling centers, results are easier to compare and possible to replicate. One result derived from these archives is that there is no single best model; one model may be best in some aspect, but turning to another aspect will highlight a different model (Gleckler et al., 2008; Reichler and Kim, 2008). Furthermore, it has been generally found that the multi-model mean outperforms the individual models (Hagedorn et al., 2005; Gleckler et al., 2008). Assigning weights to the individual models according to their historical performance (Rajagopalan et al,. 2002) can further improve upon the skill of MME relative to the simple model mean, provided that a sufficient number of hindcasts exist to distinguish the relative performance between models, i.e., about 40–50 years. Due to the need to fully cross-validate the weights assigned to models in the combination, it becomes difficult to improve upon the simple multi-model mean for MME with shorter hindcast histories (DelSole, 2007). The degree to which performance can be improved, both in terms of mean error reduction and probabilistic reliability, depends on the number of models involved, with more models yielding a higher quality MME (Robertson et al., 2004). However, it is not clear at what number the incremental benefit from adding more models begins to plateau. The magnitude of the benefit varies with the forecast target, including variable, region, and season, and with the quality of the individual models that contribute. The wide community involvement in MME has shown that: All models do have their deficiencies; the one weak point in the premise of MME is that models often do contain some common biases (Gleckler et al., 2008). It therefore makes good sense to calibrate models in terms of both their mean and variability to the greatest extent possible, prior to combination (e.g. Hagedorn et al., 2005). Hindcast records are necessary to assess model performance prior to its inclusion in an MME. The hindcast may not be long enough for the purposes of weighting models, but it
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability needs to be long enough to vet the realism of the model’s mean state and variability relative to other models in the MME suite because poor models will degrade forecast quality. Forecasts that objectively combine a number of prediction inputs allow information with different strengths and weakness to be distilled and yield more robust and reliable results. The prediction inputs can include statistical models, dynamical models, and the combination of the two. The main weakness of MME is a lack of design behind the specific models included; MME usually draws on whatever respectable models are available, and thus does not necessarily span all uncertainties in model physics. Consideration of End User Other approaches exist for going beyond the quality of a given forecast or model prediction to determine its value to a potential user. The provision of quantitative, probabilistic outlooks of societally-relevant variables can increase the use of climate forecasts even if the underlying quality were unchanged. Although seasonal climate forecasts are now commonly issued as probabilities for pre-defined categories (Barnston et al., 1999; Mason et al., 1999), those categories may not align with the risks and benefits of many decision makers. Additionally, users of the climate forecasts, from sectoral experts to the media, are often interested in relatively high resolution information that can be relevant to local concerns, even if it means reduced accuracy of the information. This information mismatch is one of the most commonly cited reasons for not using seasonal forecasts (e.g. CCSP, 2008). Good quality intraseasonal-to-interannual forecasts are only a starting point. In order for forecast information to be incorporated into climate risk management and decision making, it has to be in an appropriate format, at an appropriate space and time scale, and of the right variables to mesh with the decision models it is to inform. One way to address the information mismatch between the coarse spatial resolution of global seasonal climate forecasts and the high-resolution needs of the end user is to use downscaling techniques. In statistical downscaling, the global climate forecast provides the input parameters for an empirical model with high spatial resolution. In dynamical downscaling, the global forecast is used to provide lateral boundary conditions to a high-resolution nested regional atmospheric model. Although downscaling has been used extensively in climate change research, its use on ISI timescales has been more on an exploratory basis. With increases in computing power, global climate models are starting to close the gap with the fine spatial resolution needs of the end user. However, there is still a window of a decade or so during which downscaling techniques will continue to add significant value to the dissemination of ISI forecasts. Recent research has opened other possibilities of providing richer seasonal climate information. For example, the provision of the seasonal forecast as the full probability distribution as opposed to fixed, relative categories permits the determination of probabilistic risk of some decision-specific threshold (e.g. Barnston et al., 2000). Or, one may desire the characterization of the weather within the climate, such as the likely number of dry spells of a given duration. In some cases, certain weather characteristics of the seasonal climate may even be more predictable than the seasonal totals (e.g. Sun et al., 2005; Robertson et al., 2009). Similarly, Higgins et al. (2002, 2007) have documented how the character of daily weather changes over the United States during ENSO events. This information could complement
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability forecasts of the seasonal mean in ENSO years, particularly for the winter season, and provide true forecasts of opportunity (Livezey and Timofeyeva, 2008) if it were packaged and communicated in that manner. Users can improve the application of forecast information if they are made aware of instances of conditional forecast skill (Frias et al., 2010) or forecasts with no skill. As shown in Figure 3.13, forecasts are often more skillful during ENSO events, which could guide decision makers to selectively use forecast information as part of their planning. Likewise, there may be certain regions or situations for which forecasts, or specific improvements to the building blocks of forecasts, offer little or no skill. For example, information on soil moisture can contribute to predictions of air temperature (see the soil moisture case study in Chapter 4; Figure 4.11), but the improvements are limited to certain key regions and seasons. In regions and seasons for which there is no forecast skill, or in situations where there is no forecast signal, operational centers can still provide a useful service through the issuance of information on the historical range of possible climate outcomes (i.e., climatology). The difficulty for forecast centers in producing tailored forecasts is that what is needed is often specific to a particular problem, which in turn depends on the sector and location. This can be difficult for national or even regional forecast centers to provide on an operational basis. If the forecast data and the associated history are openly available, the tailoring of the information to the specific uses may be possible. The actual tailoring may be conducted by local forecast centers, intermediaries, or directly by the end-users. The national and international forecast centers could provide sufficient information through data archives, such that forecasts can be tailored to more specific decisions. This is not a trivial activity, however. Financial and computing resources would be required to maintain such a service. Given the investments that have already contributed to the development of intraseasonal to interannual prediction information, such an infrastructure would be a very economical extension that could dramatically increase the use of climate forecasts. For example, users would be able to evaluate past performance in terms of their own relevant metrics, or even in terms of their own local or regional observational data. Forecast centers regularly assess the quality of their prediction models or forecast systems (O’Lenic et al., 2008; Barnston et al., 2009), which is necessary for their own feedback and interaction with the climate community. However, the value of access to data for verification, tailoring, or even just formatting should not be underestimated. EXAMPLE OF AN ISI FORECAST SYSTEM The building blocks of ISI forecasts systems have been described in detail above. Here we provide a specific example of how these basic building blocks ultimately culminate in an ISI prediction. This example is based on current operational forecasts at NCEP. The intent here is to highlight the complexity of the problem, the multitude of inputs to the process, and where and when subjective input is used. A flow chart for the forecast production procedure is given in Figure 3.17. The forecast production process is described in detail in O’Lenic et al. (2008) and is summarized as follows. Climate Prediction Center operational seasonal forecasts are issued on the 3rd Thursday of each month at 8:30 AM, and a team of 7 forecasters at CPC rotates throughout the course of the year in preparing these forecasts. The process begins with a
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability FIGURE 3.17 Graphical representation of the NCEP forecast system, showing the relationship among observations, climate system models, and data assimilation schemes as well as the steps where subjective judgment and verification are used. SOURCE: John Gottschalck, NCEP, personal communication. comprehensive analysis of the state of the global oceans and atmosphere. This is largely based on best estimates of the current state of the climate system. Forecast tools, both CFS and statistical, are then consolidated into an objective first-guess forecast for U.S. temperature and precipitation. A telephone conference call is conducted the preceding Friday to discuss the current status of the climate system and the content of the available tools with partners in the broad climate community. Based on these discussions and the forecaster’s own interpretation of the forecast tools, the forecaster manually draws draft forecast maps for all thirteen forecast leads for both temperature and precipitation. A second conference call is then used to review the draft forecast maps with governmental climate partners only. Forecast maps are finalized and processed to produce images, raw data files, and files for the National Digital Forecast Database (NDFD) for a large range of users. Finally, the lead forecaster writes a “Prognostic Map Discussion” that includes a review of the climate system, rationale for the forecasts, and an overview of the forecast maps.
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability POTENTIAL IMPROVEMENTS TO ISI FORECAST SYSTEMS In examining the components of existing ISI forecast systems and current practices, a number of opportunities for improvement have been identified. These opportunities are summarized here in a structure that parallels the previous discussion by component of the ISI forecast system. In Chapter 4, there is more detail about three specific ISI forecast topics: ENSO, MJO, and soil moisture. The illustrative nature of the three case studies, together with the opportunities identified here, provide the foundation for the recommendations presented in Chapter 6. Observations Many observations that could potentially contribute to ISI predictions are not being assimilated into ISI forecast systems (see DA bullet below). The increase in the number of observations assimilated by ISI forecast systems has led to improvements in prediction. However, the attribution of these improvements to specific observations can be difficult to confirm. Also, study is required to determine the potential benefit for adopting new research observations as ongoing, operational climate observations to support ISI prediction. Targeted observations for specific climate processes that are poorly understood could improve dynamical models by providing more realistic initial conditions, improved parameterizations of sub-grid scale processes, and/or data to be used in validation. Sustained observations of the fluxes of heat and moisture between the atmosphere and ocean or between the land and atmosphere are useful for identifying biases and errors in dynamical models. Many processes that act to couple earth system components are poorly understood and undersampled, and observations of the coupling are needed. Statistical and Numerical Models Nonlinear statistical methods can augment linear statistics. While linear methods have been used in forecasting with moderate success in the past, positive skill is geographically dependent and primarily related to the presence of strong forcing, such as El Niño. Nonlinear techniques (e.g., nonlinear regression, neural networks, kernel methods) have been shown to be valuable in providing additional skill, especially at ISI timescales. Present statistical models are not in competition with dynamical models and can be combined usefully with dynamical models. They offer quality in certain areas where dynamical models fail and may point to areas where dynamical models can be improved.
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability Proper cross-validation is an essential tool to estimate the true forecast skill. The use of repeated cross-validation on the same data, however, can inflate the estimated skill when models are tuned after each iteration. Such a process can result in overfitting. Data need to be divided into training and testing sets where the testing data are set aside for an unbiased estimation of true skill. It is acceptable to use subsets of the training data for model selection. However, the testing data have to be kept out of the tuning process and used for the final assessment of skill. Most statistical tests assume stationarity, but the climate system is not stationary on ISI timescales. Statistical tests exist that can address such non-stationarity (e.g., variance stabilization techniques, Huang et al., 2004). Non-stationarity can also be exploited to improve predictions. Dynamical models exhibit systematic errors in their representation of the mean climate, the annual cycle, and climate variability. While many of these shortcomings highlight opportunities for model improvement, they also contribute to forecast error. The physical processes associated with several sources of predictability (such as ENSO or the MJO) are not adequately simulated in numerical models. Use of multi-model ensembles in an operational setting is still in its early stages. MMEs need to be developed further and research on proper methods of selection, bias correction, and weighting will likely help improve the forecasts. Data Assimilation The most advanced data assimilation algorithms are predominantly focused on atmospheric observations, while the DA schemes tend to be less advanced for the ocean than for the atmosphere. Ideally, data assimilation would be performed for the coupled Earth system. Specifically, more work is required to identify biases in the observational data and improve the ocean models so that advanced DA techniques can be applied to ocean observations. Observations of many components of the Earth system are not part of DA algorithms. Estimates of prognostic states at the land surface (e.g. soil moisture) and cryosphere (e.g., snow, sea ice extent) are generally not assimilated with operational DA schemes. Some ocean observations are assimilated as part of operational forecasts but some are not (e.g., SSH). Forecast Verification and Provision Forecast quality assessment needs to be made and communicated through multiple metrics. Forecast quality has often been expressed through a single method (e.g., Heidke skill). Multiple metrics and graphical techniques, including ones that assess the quality of the probabilistic information, will provide a better assessment of the fidelity of the forecast system.
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Assessment of Interseasonal to Interannual Climate Prediction and Predictability Access to archived hindcasts and real-time forecasts is required to tailor climate information to the needs of decision makers. Information regarding forecast quality and skill varies widely among forecast systems. Comparison among systems is critical for identifying opportunities for model improvement, as well as novel combinations of forecast models that may improve quality. Subjective intervention into forecasts needs to be minimized and documented. The subjective component can limit reproducibility, restricting retrospective comparison of forecast systems. Although there are time constraints around issuing forecasts, it is helpful to have written documentation of the subjectivity of forecast preprocessing and post-processing to assess the relative performance of the inputs and outputs.