Impact Statement
Machine learning has emerged as a powerful tool for climate prediction, but the resulting models often are too complex to interpret. Methods for extracting meaningful knowledge from machine-learning models have been developed (e.g., explainable AI), but most of these methods apply only to low-dimensional outputs. In contrast, many climate applications require predicting spatial fields. This paper proposes an approach to reducing the dimension of the output by finding components with the most skill. This technique is illustrated by training separate machine-learning models at hundreds of spatial locations, and then using this technique to show that only a few patterns are predicted with significant skill. Individual patterns can then be analyzed using regression techniques to diagnose the source of the skill.
1. Introduction
Machine-learning techniques can produce climate forecasts that outperform predictions made by state-of-the-art numerical forecast models (Hwang et al., Reference Hwang, Orenstein, Cohen, Pfeiffer and Mackey2019). Nevertheless, machine-learning models are criticized because they are not based on physics and are often difficult to interpret. Regardless of these criticisms, if a machine-learning model consistently outperforms the best physics-based model, then it has the potential to lead to deeper scientific understanding of processes driving predictable variations in the climate system. The question arises as to how to extract scientifically meaningful information from machine-learning models. Methods for extracting such information are often called explainable Artificial Intelligence, or explainable AI.
A variety of approaches have been proposed in explainable AI. One approach is to examine the sensitivity of a machine-learning model to changes in the training set. For instance, if certain predictors are critical to the skill of a model, then removing them ought to reduce the skill of the model. Various methods for ranking predictor importance involve sequential forward and backward selection methods and permutation methods (McGovern et al., Reference McGovern, Lagerquist, Gagne, Jergensen, Elmore, Homeyer and Smith2019). Unfortunately, such methods are computationally ineffective to implement in deep learning models (McGovern et al., Reference McGovern, Lagerquist, Gagne, Jergensen, Elmore, Homeyer and Smith2019; Toms et al., Reference Toms, Barnes and Ebert-Uphoff2020). Also, it is well known that forward and backward selection lead to misleading interpretations in linear regression (see Harrell, Reference Harrell2001, p. 56), and these problems will certainly be amplified in machine learning where thousands of predictors are common. Moreover, predictor importance typically varies with predictand, which complicates interpretability in multivariate prediction.
Another approach to explainable AI is backward optimization. This method determines the input pattern that most closely reproduces a given output from a trained neural network (e.g., McGovern et al., Reference McGovern, Lagerquist, Gagne, Jergensen, Elmore, Homeyer and Smith2019; Toms et al., Reference Toms, Barnes and Ebert-Uphoff2020). This approach may provide clues as to which features in the input of a neural network are important for producing a given output. On the other hand, the result may be difficult to interpret if multiple modes of variability contribute to the output, in which case the input pattern represents a mixture of modes. A related technique is layer-wise relevance propagation, which produces a heat map in the dimensions of the original input that identifies the input features most relevant for the network output (Toms et al., Reference Toms, Barnes and Ebert-Uphoff2020). Both methods are often used in classification problems with few output categories (Gange et al., Reference Gagne, Haupt, Nychka and Thompson2019; McGovern et al., Reference McGovern, Lagerquist, Gagne, Jergensen, Elmore, Homeyer and Smith2019; Toms et al., Reference Toms, Barnes and Ebert-Uphoff2020). However, for climate predictions targeting large geographical area, there are a large number of outputs, in which case it is unclear how effective these methods would be in aiding in interpretation.
In this paper, we are concerned with diagnosing and interpreting the skill of a model that predicts an entire spatial field, such as surface temperature over a geographic region. We are particularly interested in sub-seasonal predictions, for instance, predicting week 3–4 temperature, where the skill is low when measured with respect to local measures of normalized mean square error (NMSE) or correlation. The low skill locally does not preclude the existence of predictable large-scale patterns, since a significant source of sub-seasonal predictability comes from large-scale atmospheric teleconnections (National Academies of Sciences, Engineering, and Medicine, 2016). Despite the low skill as measured by these metrics, it is possible that a large-scale pattern is predictable, but this predictability is obscured locally by unpredictable weather variability that dominates at each grid point. The question arises as to whether this predictable large-scale pattern, if it exists, can be extracted from the forecast data. Renwick and Wallace (Reference Renwick and Wallace1995) review various approaches to extracting such patterns. Here, we focus on a method due to Déqué (Reference Déqué1988), which we call skill component analysis (SCA), following DelSole and Tippett (Reference DelSole and Tippett2022), who review this method. SCA finds linear combinations of data that minimize the NMSE. This methodology is analogous to predictable component analysis (PrCA), except PrCA yields eigenvectors that maximize predictability, which is distinct from skill (DelSole and Chang, Reference DelSole and Chang2003; DelSole and Tippett, Reference DelSole and Tippett2022).
One of the limitations of SCA, and other methods based on decomposing covariance matrices (e.g., CCA), is the need for relatively large amounts of data. Recently, Trenary and DelSole (Reference Trenary and DelSole2022) derived a machine-learning model by training it on thousands of years of daily data from a multi-model set of physics-based simulations. The resulting machine-learning model could skillfully predict observed week 3–4 temperature at very localized areas in the western US during winter, despite never being trained on observations. That said, the average skill across the target region is indistinguishable from a no-skill forecast (i.e. the spatially averaged NMSE is >1). The large CMIP6 training set provides an opportunity to test the ability of SCA to extract low-dimensional predictable components of a machine-learning model for forecasts characterized by low spatially-averaged.
The purpose of this paper is to apply SCA to a machine-learning model for sub-seasonal prediction. The SCA is used to diagnose the components of temperature that are most skillfully predicted by a machine-learning model and to infer the source(s) of predictability associated with these patterns. In Section 2, we describe the data sets and statistical model for predicting observed wintertime sub-seasonal temperature over the western US. In Section 3, we describe SCA, and in Section 4 we use the technique to identify the most skillfully predicted large-scale temperature patterns in the statistical forecast system of Trenary and DelSole (Reference Trenary and DelSole2022). The paper concludes with a summary of our major results.
2. Data and Methods
2.1. Statistical forecast system
The SCA is a method to identify skillfully predicted patterns in any forecast system. For illustrative purposes, we examine predictability for a statistical forecast system developed in Trenary and DelSole (Reference Trenary and DelSole2022). Their forecast system targets observed week 3–4 wintertime temperature anomalies over the western United States and is composed of 499 grid-point lasso regression models. The lasso regression coefficients are found by minimizing the cost function locally, which is referred to as the “single-task” formulation of lasso in Trenary and DelSole (Reference Trenary and DelSole2022). The predictors for this forecast system are large-scale SST anomalies in the Pacific and Atlantic Oceans, which are represented by 50 laplacian time series for each basin, giving a total of 100 SST predictors. Importantly, the lasso regression model was trained on dynamical model simulations, and then used to predict observations. More precisely, each grid-point model is trained on pre-industrial control simulations from 13 models from the Climate Model Inter-comparison Project phase 6 (CMIP6) archive (Eyring et al., Reference Eyring, Bony, Meehl, Senior, Stevens, Stouffer and Taylor2016), comprising a total of 6,889 years of daily data. The target and predictors are 2-week means and predictions target December–February. Anomalies are defined with respect to a climatology estimated as a 5th order polynomial fit in time across all 2-week means between December and February. This statistical model is referred to as CMIP6-single-task model in Trenary and DelSole (Reference Trenary and DelSole2022) and was the best performing model in that study. Since this is the only model examined in the present paper, this model will be referred to as simply the statistical model. Further details of the statistical models and observations, as well as justifications for the particular choices in the model and analysis, can be found in Trenary and DelSole (Reference Trenary and DelSole2022).
3. Skill Component Analysis
As discussed in Trenary and DelSole (Reference Trenary and DelSole2022) the above statistical model had a spatially averaged correlation less than 0.1 and a spatially averaged NMSE indistinguishable from the no-skill value of one. This low skill for sub-seasonal prediction is consistent with previous studies (e.g., DelSole and Banerjee, Reference DelSole and Banerjee2017; Hwang et al., Reference Hwang, Orenstein, Cohen, Pfeiffer and Mackey2019; Pegion et al., Reference Pegion, Kirtman, Becker, Collins, LaJoie, Burgman, Bell, DelSole, Min, Zhu, Li, Sinsky, Guan, Gottschalck, Metzger, Barton, Achuthavarier, Marshak, Koster, Lin, Gagnon, Bell, Tippett, Robertson, Sun, Benjamin, Green, Bleck and Kim2019; He et al., Reference He, Li, Trenary, Cash, DelSole and Banerjee2021). As discussed in the introduction, the apparent low skill might be an artifact of the choice of skill measure. To overcome this limitation, we find the linear combination of variables that minimize the NMSE. Let
$ t $
and
$ s $
denote the temporal and spatial indices, where
$ t=1,\dots, T $
and
$ s=1,\dots, \hskip0.35em S $
. Let
$ F\left(s,t\right) $
and
$ V\left(s,t\right) $
denote the anomaly forecast and target variables, respectively. In the context of analyzing CMIP6 data,
$ F\left(s,t\right) $
denotes predictions from the statistical model of variables in the CMIP6 simulations, and
$ V\left(s,t\right) $
denotes the corresponding verifications in the CMIP6 simulations. Then a linear combination over space is
$$ {r}_V(t)=\sum \limits_{s=1}^Sq(s)V(s,t)\hskip1em \mathrm{a}\mathrm{n}\mathrm{d}\hskip1em {r}_E(t)=\sum \limits_{s=1}^Sq(s)(F(s,t)-V(s,t)), $$
where
$ q(s) $
contains the linear coefficients. The
$ \mathrm{NMSE} $
associated with this component can be written as
$$ \mathrm{NMSE}\hskip0.35em =\hskip0.35em \frac{{\mathbf{q}}^T{\Sigma}_E\mathbf{q}}{{\mathbf{q}}^T{\Sigma}_V\mathbf{q}}, $$
where
$ {\Sigma}_E $
and
$ {\Sigma}_V $
are the sample covariance matrices of
$ \left(F\left(s,t\right)-V\left(s,t\right)\right) $
and
$ V\left(s,t\right) $
, respectively. In SCA, we seek the
$ \mathbf{q} $
that minimizes the
$ \mathrm{NMSE} $
in equation (2). Following DelSole and Tippett (Reference DelSole and Tippett2022), this minimization problem leads to the generalized eigenvalue problem
$$ {\Sigma}_E\mathbf{q}\hskip0.35em =\hskip0.35em \lambda {\Sigma}_V\mathbf{q}. $$
Typically, this eigenvalue problem has
$ S $
distinct solutions, where the eigenvalue
$ \lambda $
gives the value of
$ \mathrm{NMSE} $
corresponding to a given eigenvector
$ \mathbf{q} $
. Accordingly, the eigenvalues are ordered from smallest to largest,
$ {\lambda}_1<\dots <{\lambda}_S $
, and the corresponding eigenvectors are denoted
$ {\mathbf{q}}_1,\dots, \hskip0.35em {\mathbf{q}}_S $
. The first eigenvector has the smallest possible
$ \mathrm{NMSE} $
and is therefore the most skillful component. The associated time series for this component is obtained from equation (1). The second eigenvector gives the smallest
$ \mathrm{NMSE} $
out of all combinations whose time series are uncorrelated with the first, and is therefore the second most skillfully predicted pattern, and so on. This methodology is analogous to PrCA, except PrCA, yields eigenvectors that maximize predictability, which is distinct from skill (DelSole and Chang, Reference DelSole and Chang2003; DelSole and Tippett, Reference DelSole and Tippett2022). Note that unlike EOF analysis where the eigenvectors and principal component time series are separately orthogonal, in SCA only the time series are uncorrelated.
For our problem, the CMIP6 data set is sufficiently large that the covariance matrices are non-singular. Nevertheless, applying SCA to CMIP6 data yields components that are skillful in CMIP6, but have no skill in observational data. We interpreted this result to mean that the SCA overfit toward the sample. To mitigate overfitting, we project the western US data onto the leading 50 Laplacian eigenvectors prior to performing SCA. See DelSole and Tippett (Reference DelSole and Tippett2015) for a description of Laplacian eigenvectors and associated algorithm for deriving them. Our main conclusions showed little sensitivity for truncations between 20 and 50.
The significance of a given value of
$ \mathrm{NMSE} $
was evaluated with respect to the sampling distribution of the eigenvalues under the null hypothesis of no skill. To do so, we randomly sample pairs of forecasts and verifications in the CMIP6 data set. The forecast-verification pairs occurring within a given winter are selected together, to preserve weekly serial correlations (if any). We then randomly shuffle the years for the forecast data to misalign the forecast and verification data, and then perform SCA on this data set. This process is repeated 5,000 times to build up an empirical distribution for the individual eigenvalues. An SCA component is considered significant if its
$ \mathrm{NMSE} $
falls below the 5th percentile from the distribution of randomly shuffled data.
4. Results
The minimized NMSE from SCA is shown in Figure 1, where the black asterisks denote the eigenvalues (or optimized
$ \mathrm{NMSE} $
) and the red curve is the 5th percentile from the randomized forecasts. Note the significance threshold exceeds one for the highest-order components. This is expected for uncorrelated forecast-verification pairs, in which the expected
$ \mathrm{NMSE} $
of a randomly chosen variable is
$ 1+\frac{\mathit{\operatorname{var}}(F)}{\mathit{\operatorname{var}}(V)} $
. A component is defined to have skill only if the eigenvalue is less than one and lies below the significance threshold. Under this criterion, there are several components with skill, but only the first two modes are well separated. The 49th component deviates from the significance curve for reasons that are unknown to us, but it is an isolated component at the most extreme no-skill limit (its
$ \mathrm{NMSE} $
is 1.4), so it has no bearing to understanding sub-seasonal skill. The spatial patterns of the two leading components are shown in Figure 2. These two patterns are estimated for CMIP6 data by regressing the 6,889-year time series of the leading two component time series derived from equation (1) onto the multi-model CMIP6 temperature anomaly data. The pattern of the most skillfully predicted pattern is shown in Figure 2a and is similar to the canonical El Nino-Southern Oscillation (ENSO) teleconnection patterns (e.g., Trenberth et al., Reference Trenberth, Branstator, Karoly, Kumar, Lau and Ropelewski1998). The second most skillfully predicted pattern is shown in Figure 2b and projects strongly onto the leading mode and the ENSO teleconnection pattern.
Figure 1. Multi-model NMSE (black) recovered from skill component analysis and multi-model 5% significance level (red). Significance is estimated by the Monte Carlo method using 5,000 iterations. Analysis is performed using independently sampled data (once per winter) over the entire multi-model record. A mode is considered significant if it is less than one.
Figure 2. Patterns for the (a) 1st (a) and(b) 2nd leading skill components recovered from multi-model CMIP6 data. Predictions are made by the same CMIP6-single-task model.
We next perform a similar calculation using observations. Specifically, we first compute the linear combination as in equation (1), by projecting observational verification (
$ V\left(s,t\right) $
) and forecast (
$ F\left(s,t\right) $
) data onto the eigenvectors
$ \mathbf{q} $
found when SCA is applied to CMIP6 data. This produces a time series of the observed component (
$ r(t) $
), which we then regress onto observed grid point temperature data over the western US to yield the spatial pattern of the component. The resulting pattern for the leading SCA component is shown in Figure 3a. Unlike the model results that are characterized by a meridional temperature dipole (Figure 2a), the most skillfully predicted pattern recovered from observations is a zonally oriented temperature dipole. Why are the patterns different? Only 19 years of observed data are used to estimate this pattern and it is possible that the sample size may impact the recovered pattern. To test this, a climate model is randomly selected, in this case GFDL-ESM4, and the pattern of the leading component is estimated for different 19 year periods. Some representative results, shown in Figure 3b,c, indicate that the most skillfully predicted pattern is sensitive to sampling. As such, we cannot conclude that the dynamical model and observed patterns are different, rather the difference is likely an artifact of sample size.
Figure 3. Patterns for the 1st skill component for (a) observations and two different randomly selected 19 year segments of data from the GFDL-ESM4 model (b) and (c).
With the leading two components identified (Figure 3a,b), we now quantify how well the grid-point based models predict these large scale patterns. The predictions and associated verification data are both projected onto the SCA eigenvectors derived from CMIP6 models and the correlation between the two time series is computed. The CMIP6 model data are sampled to have the equivalent number of years as observations (i.e., 19 years) when estimating the correlation.
The results for the first and second components are shown in Figure 4a,b, respectively. The distribution of correlation coefficients found for predictions in each CMIP6 model are denoted by the vertical bars, which represent the 5th and 95th percentiles, and the mean correlation is denoted by the black asterisks. The dashed lines in Figure 4a,b denote the correlation for predictions of observations. For the leading component, shown in Figure 4a, the distribution of correlations overlaps for the different dynamical models, indicating that there is consistency in the predictability of this large-scale pattern across the dynamical models. For all but three of the dynamical models, the distribution of correlations includes observations, indicating consistency in the predictive skill of this pattern in observations and within 10 CMIP6 models. The predictive skill of this pattern differs from observations in the two CNRM models and the MRI model, suggesting that these particular dynamical models may be deficient in simulating the physical processes contributing to the predictability of this pattern. That said, it is worth noting that the skill in predicting this pattern is significantly larger than the skill based on the spatial average of local correlations, which were less than 0.1 (see Figure 5, Trenary and DelSole, Reference Trenary and DelSole2022). Moreover, the distributions of correlations in all but one of the dynamical models are distinct from zero, indicating that a robust source of predictable variations of western US surface temperature exists in a majority of the dynamical models and is linked to the same pattern. This analysis confirms that the grid-point lasso models trained on CMIP6 data are skillfully predicting a large-scale pattern. Moreover, this skill associated with prediction of this pattern is generally consistent across dynamical models and with observations.
Figure 4. Correlation between the prediction and verification data for the (a) 1st and (b) 2nd leading skill components. These correlations are found by projecting both prediction and verification data onto the leading eigenvectors recovered from SCA and correlating the resulting time series. Predictions for CMIP6 and observations are made by the same CMIP6-single-task forecast model. The black vertical bars show the 5th–95th percentile range of correlations for predictions within the specified CMIP6 model. The individual CMIP6 models are sampled to have the same number of years as observation (19 years). The black asterisk denotes the mean correlation. For observational based estimates, the time series of the SCA components are found by projecting observational forecast and verification onto the CMIP6 derived eigenvectors. The correlation for predictions using observational data for the 2000–2018 are shown as the dashed line. The skill component analysis was performed using 50 Laplacian time series.
Figure 5. Regression of the leading skill component derived from the CMIP6 single-task models onto sea surface temperature from (a) multi-model CMIP6 data and (b) observations.
The range of correlations for the second component, shown in Figure 4b, are generally reduced relative to the 1st and the skill in predicting this mode remains consistent across the climate model. However, there are several dynamical models that include zero, indicating that the skill in predicting this pattern is not significant.
Lastly, to determine the source of predictability, the time series of the leading component recovered from multi-model CMIP6 data and observations is regressed onto their respective SST anomalies. Note that the SCA eigenvectors are derived from the CMIP6 data and isolate the predictive relations between SSTs and western US wintertime temperatures identified in the climate models. This being the case, consistency in the regression patterns found for CMIP6 and observations suggests a common forcing. The resulting regression maps are shown in Figure 5a,b. The most prominent feature of these maps is the Pacific ENSO pattern which is identified in both datasets. A tripole-like pattern consistent with the North Atlantic Oscillation (NAO) forcing of Atlantic SST is visible in observations (Figure 5b), but not the climate models. More generally, differences in the regression patterns found in Figure 5a,b suggest there are differences in how regional SST variability impacts western US wintertime temperatures within climate models and the observed climate. The prominence of the ENSO-SST pattern is not surprising given that the SCA patterns resemble the traditional canonical teleconnection patterns. The regression pattern recovered for the 2nd component (not shown) also projects strongly onto ENSO. The above analysis was repeated by regressing the leading component associated with the predicted western US surface temperatures onto SST and similar patterns were recovered. This analysis suggests that the skillful prediction is associated with ENSO. A previous study by DelSole et al. (Reference DelSole, Trenary, Tippett and Pegion2017), similarly found that sub-seasonal predictability of wintertime temperatures over the US can largely be attributed to ENSO. It is perhaps not surprising that ENSO is a major source of predictability for sub-seasonal forecasts, when it is the dominant source of predictability on seasonal timescales (National Academies of Sciences, Engineering, and Medicine, 2016). That said, it is worth noting that the statistical model analyzed here outperforms a benchmark forecast where the Nino3.4 index is the sole predictor (see Figure 5, Trenary and DelSole, Reference Trenary and DelSole2022). This indicates that the ENSO-related SST variations impacting predictability are not entirely captured by the Nino3.4 index. To test this, the CMIP6-single-task predictions for CMIP6 and observation data are projected onto the SCA eigenvectors and then correlated with the associated Nino3.4 index. The correlation is 0.86 for observations and 0.67 for CMIP6. This confirms that the skill in predictability is linked to ENSO in both observations and CMIP6, but the Nino3.4 index only captures some fraction of the relevant SST variations. It is noteworthy that the skill and source of predictability for large-scale temperature variations over the western US are consistent between CMIP6 models and observations. For one, this suggests that climate models are capable of simulating the key processes driving the predictable variations in the target region on subseasonal timescales. In turn, these climate models are suitable for in-depth process-level analyses of predictability on this timescale. On the other hand, dynamical models are not perfect, and statistical models trained on dynamical models will inherit these imperfections. Perhaps a two-step procedure in which the statistical model is first trained on long dynamical model simulations, and then partly corrected based on observations, can produce an even better statistical prediction model (such as in transfer learning).
5. Conclusion
This paper proposes an approach to diagnosing the skill of a machine-learning model based on finding combinations of variables that minimize the NMSE. This approach was proposed by Déqué (Reference Déqué1988) in the context of diagnosing weather prediction models and recently reviewed by DelSole and Tippett (Reference DelSole and Tippett2022). We apply the method to statistical forecasts for week 3–4 prediction of western US wintertime temperatures. This is an instructive example because the spatially averaged NMSE of these forecasts is indistinguishable from one, suggesting no skill. Despite this, the optimization technique identifies at least two large-scale temperature variations that are skillfully predicted by the machine-learning model. The apparent low skill is an artifact of the skill measure, which is computed first by evaluating skill at each grid point and then averaging this measure across grid points. Unfortunately, unpredictable weather noise dominates each grid point and thereby obscures whatever predictability may exist from large-scale teleconnection patterns. The leading pattern resembles the canonical ENSO teleconnection pattern and the skill in predicting this pattern is consistent across a majority of the different CMIP6 models and observations. Predictability of this pattern is inconsistent between three CMIP6 models and observations, suggesting that these dynamical models are deficient in simulating key physical processes that contribute to predictable variations in western US surface temperature anomalies. We further show that the source of predictability for this pattern is largely related to Pacific SST anomalies and ENSO in particular. The second most skillfully predicted component is predicted with far less skill in both observations and dynamical models, and some dynamical models demonstrate no skill in predicting this mode. As is true for the leading mode, the second mode appears to be forced by ENSO. Though these results confirm our expectations about the source of predictability in this particular case, the technique is sufficiently general that it may provide new insights into prediction problems in which the source of predictability is less well understood. Moreover, this technique has the potential to improve prediction accuracy for low-skill forecasts. For instance, in operational forecast systems, once the SCA is developed for a particular forecast system, it may be possible to improve the skill by just predicting these large-scale patterns and setting the other components to climatology. More generally, once the leading SCA components have been isolated, regression techniques can be used to diagnose the source of skill and underlying mechanisms. Lastly, it may be possible to improve forecast skill by building a regression model to predict the amplitude of the leading skill component for observations using the climate model derived SCA eigenvectors.
Acknowledgments
We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the contributing climate modeling groups for producing and making available their model output.
Author Contributions
L.T. is responsible for writing the original draft and all formal analysis and investigation. T.D. is responsible for conceptualization of the project and project supervision. Both authors contributed to revising and editing of the manuscript. All authors approved the final submitted draft.
Competing Interests
The authors declare no competing interests exist.
Data Availability Statement
For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals and data can be found here https://esgf-node.llnl.gov/projects/cmip6/. The observational data are provided by the National Oceanic and Atmospheric Administration Climate Prediction Center and be downloaded directly from https://psl.noaa.gov/data/gridded/index.html. Example codes for the SCA can be found at https://github.com/ltrenary/Skill-Component-Analysis.
Ethics Statement
The research meets all ethical guidelines, including adherence to the legal requirements of the United States.
Funding Statement
This research was supported primarily by the National Science Foundation (AGS 1822221). The views expressed herein are those of the authors and do not necessarily reflect the views of this agency.
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