By far, the most common types of sensitivity analysis are those performed one at a time (OAT) and locally. OAT sensitivity analysis involves running a model a number of times, varying each input in turn, whilst fixing other inputs at their nominal values. For example, Wild (2007) showed that the tropospheric ozone budget was highly sensitive to differences in global NOx emissions from lightning. The observation-based range of 3–8 TgN yr-1 in the magnitude of these emissions could result in a 10 % difference in predicted tropospheric ozone burden. OAT sensitivity analysis is used in a variety of research fields including environmental science (Bailis et al., 2005; Campbell et al., 2008; de Gee et al., 2008; Saltelli and Annoni, 2010), medicine (Coggan et al., 2005; Stites et al., 2007; Wu et al., 2013), economics (Ahtikoski et al., 2008) and physics (Hill et al., 2012). While the ease of implementing OAT sensitivity analysis is appealing, a major drawback of this approach is that it assumes that the model response to different inputs is independent, which in most cases is unjustified (Saltelli and Annoni, 2010) and can result in biased results (Carslaw et al., 2013).

Global sensitivity analysis (GSA) overcomes this OAT issue by quantifying the sensitivity of each input variable by averaging over the other inputs rather than fixing them at nominal values. However, the number of sensitivity analysis studies using this global method has been very small. Ferretti et al. (2016) found that out of around 1.75 million research articles surveyed up to 2014, only 1 in 20 of studies mentioning “sensitivity analysis” also use or refer to “global sensitivity analysis”. A common type of GSA is the variance-based method, which operates by apportioning the variance of the model's output into different sources of variation in the inputs. More specifically, it quantifies the sensitivity of a particular input – the percentage of the total variability in the output attributed to the changes in that input – by averaging over the other inputs rather than fixing them at specific values. The Fourier Amplitude Sensitivity Test (FAST) was one of the first of these variance-based methods (Cukier et al., 1973). The classical FAST method uses spectral analysis to apportion the variance, after first exploring the input space using sinusoidal functions of different frequencies for each input factor or dimension (Saltelli et al., 2012). Modified versions of FAST include the extended FAST (eFAST) method which improves its computational efficiency (Saltelli et al., 1999) and the random-based-design (RBD) FAST method which samples from the input space more efficiently (Tarantola et al., 2006). Another widely used GSA method is the Sobol method (Homma and Saltelli, 1996; Saltelli, 2002; Sobol, 1990), which has been found to outperform FAST (Saltelli, 2002). Most applications of the Sobol and FAST methods involve a small number of input factors. However, Mara and Tarantola (2008) carried out a 100-input sensitivity analysis using the RBD version of FAST and a modified version of the Sobol method and found that both methods gave estimates of the sensitivity indices (SIs) that were close to the known analytical solutions. A downside to the Sobol method is that a large number of runs of the model typically need to be carried out. For the model used in Mara and Tarantola (2008), 10 000 runs were required for the Sobol method but only 1000 were needed for FAST.

If a model is computationally expensive, carrying out 1000 simulations may not be feasible. A solution is to use a surrogate function for the model called a meta-model that maps the same set of inputs to the same set of outputs but is computationally much faster. Thus, much less time is required to perform GSA using the meta-model than using the slow-running model. A meta-model can be any function that maps the inputs of a model to its outputs, e.g. linear or quadratic functions, splines, neural networks. A neural network, for example, works well if there are discontinuities in the input–output mapping, but such a method can require thousands of runs of the computationally expensive model to train it (particularly if the output is highly multi-dimensional) which will likely be too time-consuming. Here, we use a statistical emulator because it requires far fewer training runs and it has two useful properties. First, an emulator is an interpolating function which means that at inputs of the expensive model that are used to train the emulator, the resulting outputs of the emulator must exactly match those of the expensive model (Iooss and Lemaître, 2015). Secondly, for inputs that the emulator is not trained at, a probability distribution of the outputs that represents their uncertainty is given (O'Hagan, 2006). The vast majority of emulators are based on Gaussian process (GP) theory due to its attractive properties (Kennedy and O'Hagan, 2000; O'Hagan, 2006; Oakley and O'Hagan, 2004), which make GP emulators easy to implement while providing accurate representations of the computationally expensive model (e.g. Chang et al., 2015; Gómez-Dans et al., 2016; Kennedy et al., 2008; Lee et al., 2013). A GP is a multivariate normal distribution applied to a function rather than a set of variables. The original GP emulator in a Bayesian setting was developed by Currin et al. (1991) (for a basic overview, see also O'Hagan, 2006) and is mathematically equivalent to the kriging interpolation methods used in geostatistics (e.g. Cressie, 1990; Ripley, 2005). Kriging regression has been used as an emulator method since the 1990s (Koehler and Owen, 1996; Welch et al., 1992). More recently, there has been considerable interest in using this kriging emulator approach for practical purposes such as GSA or inverse modelling (Marrel et al., 2009; Roustant et al., 2012). Examples of its application can be found in atmospheric modelling (Carslaw et al., 2013; Lee et al., 2013), medicine (Degroote et al., 2012) and electrical engineering (Pistone and Vicario, 2013).

For GSA studies involving multi-dimensional output, a traditional approach is to apply a separate GP emulator for each dimension of the output space. However, if the output consists of many thousands of points on a spatial map or time series (Lee et al., 2013), then the need to use thousands of emulators can impose substantial computational constraints even using the FAST methods. A solution is to adopt a GSA method that does not rely on an emulator but is based on generalised additive modelling (Mara and Tarantola, 2008; Strong et al., 2014, 2015b) or on a partial least squares approach (Chang et al., 2015; Sobie, 2009). A separate generalised additive model (GAM) can be built for each input against the output of the expensive model, and the sensitivity of the output to changes in each input is then computed using these individual GAM models. Partial least squares (PLS) is an extension of the more traditional multivariate linear regression where the number of samples (i.e. model runs in this context) can be small, and they may even be less that the number of inputs (Sobie, 2009).

An alternative way of reducing the computational constraints is to use principal component analysis (PCA) to reduce the dimensionality of the output. This means that we require far fewer emulators to represent the outputs, reducing the GSA calculations by a large margin, although there is some loss of detail. This emulator–PCA hybrid approach has been successfully used in radiative transfer models (Gómez-Dans et al., 2016), a very simple chemical reaction model (Saltelli et al., 2012) and general circulation models (Sexton et al., 2012). While we hypothesise that both emulator-free and PCA-based methods are suited to large-scale GSA problems (e.g. those involving more than 20 input factors), a focus of our work is to determine the accuracy of these methods for a smaller-scale GSA study.

Recent research comparing different GSA methods based on Gaussian process emulators has been limited in application to relatively simple models and low-dimensional output (Mara and Tarantola, 2008). Using two computationally expensive models of global atmospheric chemistry and transport – namely the Frontier Research System for Global Change/University of California at Irvine (FRSGC/UCI) and Goddard Institute for Space Studies (GISS) models – we compare the accuracy and efficiency of global sensitivity analysis using emulators and emulator-free methods, and we investigate the benefits of using PCA to reduce the number of emulators needed. We compare and contrast a number of ways of computing the first-order sensitivity indices for the expensive atmospheric models: (i) the Sobol method using an emulator, (ii) the extended FAST method using an emulator, (iii) generalised additive modelling, (iv) a partial least squares approach and (v) an emulator–PCA hybrid approach. Hereafter, we refer to (i) and (ii) as emulator-based GSA methods and (iii) and (iv) as emulator-free GSA methods.

Global atmospheric chemistry and transport models simulate the composition of trace gases in the atmosphere (e.g. O3, CH4, CO, SOx) at a given spatial resolution (latitude × longitude × altitude). The evolution in atmospheric composition over time is controlled by a range of different dynamical and chemical processes, our understanding of which remains incomplete. Trace gases are emitted from anthropogenic sources (e.g. NO from traffic and industry) and from natural sources (e.g. isoprene from vegetation, NO from lightning), they may undergo chemical transformation (e.g. formation of O3) and transport (e.g. convection or boundary layer mixing), and they may be removed through wet or dry deposition. Global sensitivity analysis is needed to understand the sensitivity of our simulations of atmospheric composition and its evolution to assumptions about these governing processes.

In this study, we performed GSA on two such atmospheric models. We used the FRSGC/UCI chemistry transport model (CTM) (Wild et al., 2004; Wild and Prather, 2000) and the GISS general circulation model (GCM) (Schmidt et al., 2014; Shindell et al., 2006). We used results from 104 model runs carried out with both of these models from a comparative GSA study (Wild et al., 2018). This involved varying eight inputs or parameters over specified ranges using a maximin Latin hypercube design: global surface NOx emissions (30–50 TgN yr-1), global lightning NOx emissions (2–8 TgN yr-1), global isoprene emissions (200–800 TgC yr-1), dry deposition rates (model value ±80 %), wet deposition rates (model value ±80 %), humidity (model value ±50 %), cloud optical depth (model value × 0.1–10) and boundary layer mixing (model value × 0.01–100). For this study, we focus on a single model output, namely the global distribution of tropospheric columns of mean methane (CH4) lifetime at the annual timescale. The CH4 lifetime is an important indicator of the amount of highly reactive hydroxyl radical in the troposphere (Voulgarakis et al., 2013), and we choose this output because of its contrasting behaviour in the two models. The native spatial resolution of the models is 2.8∘ × 2.8∘ for FRSGC and 2.5∘ × 2.0∘ for GISS, but we combine neighbouring grid points so that both models have a comparable resolution of 5–6∘, giving a total of 2048 grid points for FRSGC/UCI and 2160 grid points for GISS.

For brevity and generality, we hereafter refer to each of the atmospheric chemical transport models as a simulator. A common way of conducting global sensitivity analysis for each point in the output space of the simulator – where the output consists of, for example, a spatial map or a time series – is to compute the first-order sensitivity indices (SIs) using variance-based decomposition; this apportions the variance in simulator output (a scalar) to different sources of variation in the different model inputs. Assuming the input variables are independent of one another – which they are for this study – the first-order SI, corresponding to the ith input variable (i=1, 2, ..., p) and the jth point in the output space, is given by Si,j=VarEYj|XiVar(Yj)×100, where Xi is the ith column of the n×p matrix X (i.e. a matrix with n rows and p columns) which stores the n samples of p-dimensional inputs, and Yj is the jth column of the n×m matrix which stores the corresponding n sets of m-dimensional outputs (Table 1). We multiply by 100 so that the SI is given as a percentage. The notation given by Var(⋅) and E(⋅) denotes the mathematical operations that compute the variance and expectation. The simplest way of computing Si,j is by brute force, but this is also the most computationally intensive (Saltelli et al., 2008).

When the simulator is computationally expensive to run – like the atmospheric chemical transport models used here – we substitute it with an emulator which is a surrogate of the expensive simulator but much faster to run. If we are confident that the emulator is accurate, then we can compute the first-order SIs from the Sobol and eFAST methods using the outputs of the emulator rather than the simulator. Mathematically, an emulator is a statistical model that mimics the input–output relationship of a simulator. As stated in the introduction, an emulator is an interpolating function at model outputs it is trained at and gives a probability distribution and other outputs (O'Hagan, 2006).

An emulator is trained using N sets of p-dimensional inputs denoted by x1, x2, ..., xN and N sets of one-dimensional outputs from the simulator given by y1=f(x1), y2=f(x2), ..., yN=f(xN); f represents the simulator and for our study N=80 (see Sect. 2.6). The most common form of an emulator is a GP since it has attractive mathematical properties that allow an analytical derivation of the mean and variance of the emulated output (given by f^(x) for a general input x). A notable exception is Goldstein and Rougier (2006), who used a non-GP emulator based on a Bayes linear approach. More formally, a GP is an extension of the multivariate Gaussian distribution to infinitely many variables (Rasmussen, 2006). The multivariate Gaussian distribution is specified by a mean vector μ and covariance matrix Σ. A GP has a mean function which is typically given by m(x)=E(f(x)) and covariance function given by c(x, x′) = cov(f(x), f(x′)), where x and x′ are two different p-dimensional inputs. For the latter, we used a Matern (5/2) function (Roustant et al., 2012), which is given by cx,x′=s2+1+5x-x′θ+53x-x′θ2×exp-5x-x′θ, where s denotes the standard deviation and θ is the vector of range parameters (sometimes called length scales). These emulator parameters are normally estimated using maximum likelihood (see Bastos and O'Hagan, 2009, for details). GP emulators for uncertainty quantification were originally developed within a Bayesian framework (Currin et al., 1991; Kennedy and O'Hagan, 2000; O'Hagan, 2006; Oakley and O'Hagan, 2004).

Developed around the same time, the kriging interpolation methods used in geostatistics are mathematically equivalent to the GP methods developed by Currin et al. (1991) (e.g. Cressie, 1990; Ripley, 2005). Kriging-based emulators have been used for 25 years (Koehler and Owen, 1996; Welch et al., 1992), with recent implementations including the DICE-Kriging R packages used for GSA and inverse modelling (Marrel et al., 2009; Roustant et al., 2012). Since the latter approach is computationally faster, we adopted the DICE-Kriging version of the GP emulator for this study. For the statistical theory behind both emulator versions and descriptions of related R packages, see Hankin (2005) and Roustant et al. (2012).

For GSA studies involving highly multi-dimensional output, the time to compute the SIs can be significantly reduced by employing an emulator-free GSA approach. In this study, we consider two such methods using (i) GAM and (ii) a PLS regression approach. For both the GAM and PLS methods, we used n=N simulator runs to compute the sensitivity indices (Table 1), and for our study these were the same N=80 runs that were used to train the emulators described in Sect. 2.3. In the descriptions of these two sensitivity analysis methods (Sect. 2.4.1 and 2.4.2), we thus use X=[X1X2,…,Xp] and Y to denote the matrices that store N sets of p-dimensional inputs and m-dimensional outputs.

As an alternative approach for speeding up the sensitivity analysis calculations, we computed the SIs from the Sobol GSA method using a hybrid approach involving PCA to reduce the dimensionality of the output space, and then used separate Gaussian process emulators for each of the transformed outputs (Gómez-Dans et al., 2016; Saltelli et al., 2012; Sexton et al., 2012). After performing the emulator runs, we then reconstruct the emulator output on the original output space, from which we compute the sensitivity indices.

PCA transforms the outputs onto a projected space with maximal variance. Mathematically, we obtain the matrix of transformed outputs Y(PC) by Y(PC)=YA∗, where Y is the N×m matrix of training outputs from the simulator (see Sect. 2.3), and A∗ is a matrix whose columns are orthogonal to one another and whose ith column (Ai∗) is chosen such that var(YAi∗) is maximised subject to the constraint Ai∗TAi∗=1. The vector A1∗ is called the first principal component (PC1), and we define λ1 to be the principle eigenvalue of S= var (Y) which is the largest variance of the outputs Y with respect to PC1. The second, third, fourth columns, etc. of A* are referred to as PC2, PC3, PC4, etc. with λ2, λ3, λ4, etc. representing the second, third, fourth, etc. largest variance of Y, respectively. PC1 contains the most information in the output, followed by PC2, then PC3, etc. The number of principal components required is commonly determined by plotting the following points: (1, λ1), (2, λ1+λ2), (3, λ1+λ2+λ3), ..., and identifying the point where the line begins to flatten out. This is equivalent to choosing a cutoff when most of the variance is explained. In this study, we included the first Npc principal components such that 99 % of the variance is explained. The 99 % threshold was also necessary for this study to ensure that the reconstructed emulator output accurately approximated the simulator output for the validation runs (Fig. 2). While we found the 99 % threshold was necessary, other studies may find that a lower threshold (e.g. 95 %) is sufficient.

This technique of reducing the dimension of the output space from m=∼2000 spatially varying points to the first Npc principal components (e.g. Npc=5 for the FRSGC model; see Sect. 2.6) means that the number of required emulator runs to compute the sensitivity indices from the Sobol method is reduced by a factor of m/Npc (= ∼ 400 using above m and Npc values). However, after having generated the Npc sets of output vectors for the Sobol method (YA(PC), YB(PC), YCi(PC), YDi(PC); see Sect. 2.2), we need to reconstruct the m sets of output vectors which are required to compute the sensitivity indices for each of the m points in the output space. To do this, we first set the elements of the (Npc+1)th, (Npc+2)th, ..., m columns of the matrix A∗ (Eq. 11) to zero and call this new matrix Asample∗. We also form a n×m matrix Ysample(PC) whose first Npc columns are vectors storing the emulator outputs corresponding to the first Npc principal components, while the elements of the remaining columns are set to zero. Recall that Ysample(PC) is different from Y(PC) where the latter has N rows (80 for this study) which correspond to the number of simulator runs required to train the emulators, whereas the number of samples n (n=10 000 for this study) refers to the number of emulator runs needed to estimate the sensitivity indices. The n×m matrix Ysample of the reconstructed m-dimensional outputs is computed using Ysample=Ysample(PC)Asample∗T.

We use this formula to compute the YA, YB, YCi and YDi vectors from Sect. 2.2 and the resulting sensitivity indices using Eq. (2) from the Sobol method (Sect. 2.2).

The sequence of tasks to complete when performing global sensitivity analysis is shown schematically in Fig. 1. The choice of inputs (e.g. parameters) to include in the sensitivity analysis will depend upon which have the greatest effects, based on expert knowledge of the model and field of study. Expert judgement is also needed to define the ranges of these inputs. A space-filling design such as maximin Latin hypercube sampling or sliced Latin hypercube sampling (Ba et al., 2015) is required in order to sample from the input space with the minimum sufficient number of model runs. We used n=10 000 for the Sobol method and n=5000 for the eFAST method, but n=N=80 for the GAM and PLS methods. The third stage is to run the model at the set of input points specified by the space-filling sampling design.

If we are employing an emulator, the next stage is to build the emulator using the training runs. The number of training runs (N) is determined by N=10×p, where p is the number of input variables (Loeppky et al., 2009). We also need to perform runs of the computationally expensive simulator to validate the emulators. For this study, we ran the simulators with an additional set of inputs for validation. Comparing the emulator outputs with the simulator outputs using the validation inputs is usually sufficient, but more sophisticated diagnostics can also be carried out if needed (Bastos and O'Hagan, 2009). If employing the emulator-free approach, validation is also needed because we are using a statistical model to infer the SIs. Such a validation is not a central part of our results but is included in the Supplement (Fig. S2). For the emulator–PCA hybrid approach (Fig. 1), we found that the first 5 (for FRSGC) and 40 (for GISS) principal components were required to account for 99 % of the variance. This means that only 5–40 emulators are required to generate a global map in place of ∼ 2000 needed if each grid point is emulated separately, thus providing large computational savings.

The final stage is to compute the first-order SIs for all the inputs; these quantify the sensitivity of the output to changes in each input. The SIs are also known as the main effects. The eFAST, Sobol and GAM approaches can also be used to compute the total effects, defined as the sum of the sensitivities of the output to changes in input i on its own and interacting with other inputs. For this study, we do not consider total effects as the sum of the main effects was close to 100 % in each case.

Since the emulators we employed are based on a scalar output, we built a separate emulator for each of the ∼ 2000 model grid points to represent the spatial distribution of the CH4 lifetimes. At the 24 sets of inputs set aside for emulator validation, the predicted outputs from the emulators compared extremely well with the corresponding outputs from both chemistry models (Fig. 2a, b, R2=0.9996–0.9999, median absolute difference of 0.1–0.18 years). When PCA is used to reduce the output dimension from ∼ 2000 to 5–40 (depending on the chemistry model), the accuracy of the predicted outputs was not as good (Fig. 2c, d, R2=0.9759–0.9991, median absolute difference of 0.94–3.44 years) but was still sufficient for this study.

As expected, the two emulator-based global sensitivity analysis (GSA) approaches (eFAST and Sobol) produced almost identical global maps of first-order SIs (%) of CH4 lifetime; see Figs. 3 and 4. The statistics (mean, 95th percentile and 99th percentile) of the differences in SIs between the two GSA methods over all eight inputs at 2000 output points for the FRSGC and GISS models are shown in Fig. 5 (M1 versus M2).

Our results show that the GAM emulator-free GSA method produces very similar estimates of the SIs to the emulator-based methods (Figs. 3, 4; row a vs. c for Sobol versus GAM). The 95th and 99th percentiles of differences of the emulator-based methods (e.g. Sobol) versus GAM are 5 and 9 % for FRSGC, and 7 and 10 % for GISS (Fig. 5, M1 versus M3). For both models, the PLS non-emulator-based method produced SIs that were significantly different from those using the eFAST and Sobol methods (Figs. 3, 4; row a vs. d for Sobol vs. PLS). For FRSGC, the mean and 95th percentile of the differences in SIs for the Sobol versus PLS methods are around 21 and 31 %, while for GISS the corresponding values are around 14 and 23 % (Fig. 5, M1 versus M4). Thus, our results indicate that the PLS method is not suitable for use as an emulator-free approach to estimating the SIs.

The global map of SIs using the emulator–PCA hybrid approach compared well to those from the emulator-only approach (Figs. 3, 4; row a vs. e). The 95th and 99th percentiles of differences between the two approaches were 6 and 10 %, respectively, for FRSGC (Fig. 5a, M1 versus M5) and 3 and 5 %, respectively, for GISS (Fig. 5b, M1 versus M5). These are both higher than the corresponding values for the emulator-only methods (Fig. 5, M1 versus M2; < 2 and < 3 %, respectively). These higher values for the emulator–PCA hybrid approach are also reflected in the poorer estimates of the validation outputs using this approach versus the emulator-only approach (Fig. 2). Such poorer estimates are expected because the PCA-transformed outputs only explain 99 % of the variance of the untransformed outputs used in the emulator-only approach.

Our results align with the consensus that the eFAST method or other modified versions of the FAST method (e.g. RBD-FAST) produce very similar SIs to the Sobol method. Mathematically, the two methods are equivalent (Saltelli et al., 2012) and when the analytical (true) values of the SIs can be computed, both methods are able to accurately estimate these values (Iooss and Lemaître, 2015; Mara and Tarantola, 2008). However, many studies have noted that the Sobol method requires more simulator (or emulator) runs to compute the SIs. Saltelli et al. (2012) state that 2k×100 (%) more model runs are required for the Sobol method compared to eFAST, where k is the number of input factors (e.g. if k=8, then 25 % more runs are needed for Sobol). Mara and Tarantola (2008) found that the Sobol method required ∼ 10 000 runs of their model to achieve the same level of aggregated absolute error to that of FAST, which only needed 1000 runs. This is comparable to our analysis where the Sobol method required 18 000 runs of the emulator but only 1000 runs were needed for the eFAST method.

Given recent interest in applying GAMs to perform GSA (Strong et al., 2015a, b, 2014), only Stanfill et al. (2015) have compared how they perform against other variance-based approaches. The authors found that first-order SIs estimated from the original FAST method were very close to the true values using 600 executions of the model, whereas the GAM approach only required 90–150 model runs. This is roughly consistent with our results, as we estimated the SIs using 80 runs of the chemistry models for GAM and 1000 runs of the emulator for the eFAST method.

There are a limited number of studies comparing the accuracy of the SIs of the GAM method amongst different models, as in our study. Stanfill et al. (2015) found that the GAM method was accurate at estimating SIs based on a simple model (three to four parameters) as well as a more complex one (10 parameters). However, if more models of varying complexity and type (e.g. process versus empirical) were to apply the GAM approach, we expect that while GAM would work well for some models, for others the resulting SIs may be substantially different from those produced using the more traditional Sobol or eFAST methods. Saltelli et al. (1993) suggests that the performance of a GSA method can be model dependent, especially when the model is linear versus non-linear or monotonic versus non-monotonic, or if transformations are applied on the output (e.g. logarithms) or not. This is particularly true for GSA methods based on correlation or regression coefficients (Saltelli et al., 1999), which might explain why the SIs calculated from the PLS method in our analysis also disagreed with those of the eFAST/Sobol methods for the FRSGC versus GISS models. Not all GSA methods are model dependent; for example, the eFAST method is not (Saltelli et al., 1999).

For both chemistry models, using PCA to significantly reduce the number of emulators needed resulted in SIs very similar to those calculated using an emulator-only approach. For the GISS model, this was encouraging given that the spread of points and their bias in the emulator versus simulator scatter plot were noticeably larger than those of the FRSGC model (Fig. 2c, d). If we had increased the number of principle components so that 99.9 % of the variance in the output was captured rather than 99 %, following Verrelst et al. (2016), then we would expect less bias in the validation plot for GISS. However, the poor validation plots did not translate into poorly estimated SIs for the emulator–PCA approach. On the contrary, the estimated SIs for GISS are consistent with the estimated SIs using either emulator-only approach (Fig. 5).

The use of PCA in variance-based global sensitivity analysis studies is relatively new but has great potential for application in other settings. De Lozzo and Marrel (2017) used an atmospheric gas dispersion model to simulate the evolution and spatial distribution of a radioactive gas into the atmosphere following a chemical leak. The authors used principal component analysis to reduce the dimension of the spatio-temporal output map of gas concentrations to speed up the computation of the Sobol sensitivity indices for each of the ∼ 19 000 points in the output space. This emulator–PCA hybrid approach was also used to estimate the Sobol sensitivity indices corresponding to a flood forecasting model that simulates the water level of a river at 14 different points along its length (Roy et al., 2017). Using a crop model to simulate a variable related to nitrogen content of a crop over a growing season of 170 days, Lamboni et al. (2011) using PCA to reduce the dimension of the output space. However, unlike other comparable studies, the computed sensitivity indices corresponded to the principal components, i.e. to a linear combination of the 170 output values. This is permissible if the principal components can be interpreted in some physical sense. For Lamboni et al. (2011), the first PC approximately corresponded to mean nitrogen content over the whole growing season, while the second PC was the difference in nitrogen content between the first and second halves of the growing season.

Our work extends the work of Wild et al. (2018) who used the same training inputs and the same atmospheric chemical transport models (FRSGC and GISS) but different outputs. Instead of using highly multi-dimensional output of tropospheric methane lifetime values at different spatial locations, Wild et al. (2018) used a one-dimensional output of global tropospheric methane lifetime. Using the eFAST method, the authors found that global methane lifetime was most sensitive to change in the humidity input for the FRSGC model, while for the GISS model the surface NOx and the lightning NOx inputs were most important for predicting methane lifetime at the global scale, followed by the isoprene, the boundary layer mixing and the humidity inputs (Wild et al., 2018). As expected, our results indicated that these same inputs explained most of the variance in the outputs for the different spatial locations. However, while the humidity SI for GISS was very low at the global scale (SI of 5 %), out study found that the SIs for humidity were very high (50–60 %) for the higher-latitude regions (Fig. 4).

GSA studies for computationally expensive models involving a small number of inputs (e.g. < 10) are useful and straightforward to implement (Lee et al., 2012). However, the inferences made are limited due to the large number of parameters on which these models depend and the number of processes that they simulate. Hence, interest is growing in carrying out large-scale GSA studies involving a high number of inputs to improve understanding of an individual model (e.g. Lee et al., 2013) or to diagnose differences between models (Wild et al., 2018). For GSA studies when the number of inputs is small, our study has demonstrated that the GAM approach is a good candidate for carrying out emulator-free GSA since it calculates very similar SIs without the computational demands of emulation. A caveat is that the performance of GAM may depend on the behaviour of the model; although we have found it is a good GSA method for our models (FRSGC and GISS) and output (CH4 lifetimes), its suitability may not be as good in all situations.