## 1. Introduction

This paper proposes a new method for representing climate data in a general domain on a sphere. The method is based on eigenfunctions of the Laplace operator, which provide a natural basis for defining orthogonal spatial patterns that can be ordered by a measure of length scale. Such representation is useful for dimension reduction. Laplacian eigenfunctions are ubiquitous in climate modeling—for example, spherical harmonics provide the basis of all working spectral models—but the use of Laplacian eigenfunctions in arbitrary domains, such as over continents or ocean separately, is not common. One reason for this neglect is that Laplacian eigenfunctions in general domains under traditional Dirichlet or Neumann boundary conditions often are difficult to obtain. In particular, an irregular boundary typically cuts between grid points on a regular grid and therefore requires special differencing schemes that depend on the geometry of the boundary relative to the grid points. The development of schemes that have the same order accuracy as the scheme in the interior and yield stable numerics is challenging (Fogelson and Keener 2001). We exploit new techniques that have been proposed in computational science and machine learning literature. The basis set we propose depends only on the shape of the domain and thus is independent of the statistics of the data and is relatively easy to compute using standard eigenvalue solvers.

A major application of the proposed basis set is dimension reduction. Climate studies often involve massive datasets. For example, a field represented on a typical 2.5° × 2.5° longitude–latitude grid comprises over 10 000 numbers per field, and if the field evolves in time then the total number of numbers representing the data easily exceeds one million, which is too many for human comprehension. Accordingly, most climate studies effectively project data onto a reduced subspace whose dimension rarely exceeds 20. Indeed, the vast majority of “climate indices” are nothing more than projections of data onto prespecified spatial structures (e.g., the global average temperature is the projection of the data onto a spatially uniform pattern). The single most common method for representing climate data on a reduced subspace is principal component analysis (PCA), which is optimal in the sense that it captures the most variance for the specified dimension number (Mardia et al. 1979). Despite this optimality, there are at least three reasons why PCA is not ideal for climate studies. First, the components depend on the data and therefore change as the time period and data source change. This dependence makes comparisons across datasets and time periods difficult. Second, PCA tends to overfit variance, which leads to biases (Lawley 1956). These biases can be important in some statistical analyses (e.g., detection and attribution; see Allen and Tett 1999). Third, components that explain relatively little variance may be important to include in certain contexts (Farrell and Ioannou 2001; Giannakis and Majda 2012).

Another attractive property of Laplacian eigenfunctions is that they can be ordered by a measure of length scale. Many important components of climate variability are characterized by large-scale patterns. For instance, the response to changing greenhouse gas concentrations has a strong projection on the spatially uniform pattern (which is the largest possible scale on a globe). Seasonal variability caused by ENSO tends to be characterized by patterns that span continental scales (Ropelewski and Halpert 1987). In addition, identifying “signals” in data often requires spatial smoothing to filter out random weather noise on short spatial scales. Many spatial smoothers can be interpreted as projecting data on large-scale spatial structures. Laplacian eigenfunctions provide an objective, intuitive basis for filtering out small-scale variability. More generally, Laplacian eigenfunctions can be used to construct “low pass,” “band pass,” and “high pass” spatial filters.

The approach we propose is based on the work of Saito (2008). This approach derives Laplacian eigenfunctions from a Green’s function. In the case of a restricted domain, the Green’s function is generally unknown and difficult to compute. Nevertheless, one can still set up an integral equation for Laplacian eigenfunctions using the known Green’s function on a sphere. Saito (2008) proves that the eigenfunctions of the resulting operator are identical to those of the Laplace operator with a nonlocal boundary condition. Although the new boundary condition is more complicated than the traditional Dirichlet or Neumann boundary conditions, it is implicit in the solution method and does not appear explicitly. Moreover, our purpose is to define an orthogonal basis set to represent large-scale fields, not to solve Laplace’s equation, so the particular boundary condition imposed on the eigenfunctions is not critical. The main differences between our approach and that of Saito (2008) are that we use the exact Green’s function on the surface of a sphere, account for singularities more systematically, and enforce the first eigenfunction to be the spatially uniform pattern.

The proposed approach is related to recent work at elucidating the geometrical structure of low-dimensional nonlinear manifolds embedded in a high-dimensional space (Belkin and Niyogi 2003; Giannakis and Majda 2012). Belkin and Niyogi (2003) proposed representing the low-dimensional manifold using a graph composed of one node for each point and an edge connecting two nodes, with an edge weight that decays with the distance between the two nodes. The Laplacian of this graph was shown to be intimately related to the Laplace–Beltrami operator on manifolds whose solution involves the Green’s function of the heat equation. For short time intervals and short distances, the Green’s function effectively corresponds to an approximately Gaussian weight decay function. The resulting eigenfunctions are discrete approximations of the eigenfunctions of the Laplace–Beltrami operator on the manifold. Coifman and Lafon (2006) proposed the concept of diffusion maps based on eigenfunctions of a Markov matrix describing a random walk on the data. Diffusion maps unified many previous methods and clarified their relation to spectral clustering and the Fokker–Planck equation (Nadler et al. 2006). Giannakis and Majda (2012) applied these concepts to time-lagged embedded state vectors to identify features characteristic of nonlinear dynamics, such as intermittency and rare events, which are not accessible by classical techniques. While these methods are important to the overall problem of data representation, they depend on the data. Here, we are interested in basis sets that do not depend on data. Accordingly, we determine Laplacian eigenfunctions of the physical domain, which depend only on the geometry of the domain and hence can be precomputed independently of the data. Importantly, the resulting eigenfunctions can be applied to small sample sizes without encountering statistical complexities that arise from data-dependent basis vectors. Since the geometry of a sphere is known, we use the exact Green’s function for a sphere rather than a Gaussian approximation. In separate analysis (not shown), similar results were found using the Gaussian approximation. However, the Gaussian approximation requires specifying an arbitrary parameter, which effectively corresponds to the time scale of the diffusion process, whereas the method proposed here does not involve any tunable parameters. Also, since we use the exact Green’s function, our eigenfunctions converge to familiar spherical harmonics when the domain is the whole globe (as will be shown).

The next section describes our methodology in detail. Following that, section 3 compares the Laplacian eigenfunctions computed from our method to those computed from the discretized Laplace operator on a globe. It is shown that the resulting eigenvectors and eigenvalues are very similar, which validates the numerical method, at least on a globe, despite being derived from two very different calculations. Section 4 shows that the leading Laplacian eigenfunctions of a high-resolution grid can be obtained quite accurately from a coarsened grid. Section 5 discusses certain mathematical questions that arise from the geometry of the boundary conditions. Sections 6 and 7 show the results of applying Laplacian eigenfunctions to describe the variability of surface temperature and precipitation. We conclude with a summary of our results.

## 2. Laplacian eigenfunctions on the surface of a sphere

*θ*and

*ϕ*and are latitude and longitude, respectively. The eigenfunctions of the Laplace operator satisfy

*λ*is an eigenvalue. There are at least two numerical methods for finding eigenfunctions of the Laplace operator: one based on the differential operator (1) and one based on the integral operator. We use the integral operator, which requires knowing the Green’s function. The Green’s function

*f*is a suitable forcing function, is given by

*ρ*is the great circle distance between the points

*G*is a symmetric operator. Unfortunately, the kernel

*β*is a constant to be determined later, in which case the integral (5) becomes

*K*is symmetric, the theorems on symmetric kernels can be used; in particular, the kernel has a complete orthonormal basis and can be represented by an eigenfunction expansion.

*S*is the total number of grid cells. Let the value of

*Y*at the center of the

*i*th grid cell be

*j*and response at

*i*be

*i*th grid cell, the integral (5) is

*W*is the antiderivative of

*i*th grid cell:

*γ*is an eigenvalue of

*S*eigenvectors

*i*th diagonal element is

**j**, which is an

*S*dimensional vector of all ones. Let the transformed eigenvector be

*α*is a normalization constant chosen to give

*α*is ignored because eigenvectors are unique up to a multiplicative factor). Therefore, we compute the

*i*th largest eigenvalue of

*L*

_{2}norm. It is natural to normalize each eigenvector

*β*to satisfy (6), namely,

*S*-dimensional state vector

*N*state vectors, say

*N*-dimensional vector denoting the

*k*th row of

*j*th Laplacian eigenfunction, and the fraction of variance explained by the

*j*th Laplacian eigenfunction is

As an illustration, we computed the Laplacian eigenfunctions over the complete globe on a 5° × 5° regular grid. The leading Laplacian eigenfunctions, shown in Fig. 1, are virtually identical to the exact spherical harmonics sampled on the grid.

Now consider Laplacian eigenfunctions over a restricted domain. In this case, the Green’s function is generally unknown and difficult to compute. Nevertheless, the eigenvalue problem (21) can still be set up by including only those grid points inside the domain. Saito (2008) showed that the continuous version of this new operator commutes with the Laplace operator, provided the solution satisfies certain nonlocal boundary conditions. A standard theorem in functional analysis states that if two operators commute, then they share the same eigenfunctions. Thus, one can find the eigenfunctions of whichever operator is easier to solve. The Green’s function approach is especially simple because it requires only the location of the grid points and pairwise distances. In particular, the nonlocal boundary conditions are implicit and are not used explicitly to obtain the eigenfunctions. Although the resulting eigenfunctions satisfy neither the Dirichlet nor the Neumann boundary conditions, they still define an orthogonal basis set that can be ordered by length scale. Since our purpose is to define an orthogonal basis set to represent large-scale fields, not to solve Laplace’s equation, the particular boundary condition imposed on the eigenfunctions is not critical, as long as it does not eliminate important modes of variability. In the above method, the derived basis set is complete, so all modes of variability are captured. The main differences between our approach and that of Saito (2008) are that we use the exact Green’s function on the surface of a sphere, account for singularities along the diagonal more systematically, and enforce the first eigenfunction to be the spatially uniform pattern.

Some mathematical subtleties related to the smoothness and connectedness of the boundary conditions are worth discussing. We defer this discussion to section 5 so that we can complete our description of the method for readers interested in practical applications.

## 3. Comparison with eigenvectors of the discretized Laplace operator

*Y*at the

*i*th longitude and

*j*th latitude. Let

*I*and

*J*denote the total number of longitudes and latitudes, respectively. A second-order differencing scheme for the Laplace operator (1) is

*j*. The grid is defined such that the poles lie at

*i*in the sense that

## 4. Numerical considerations

Perhaps the most important limitation of the above methodology is that it requires solving the eigenvectors of a matrix whose dimension equals the number of grid points. For a global domain on a 2.5° × 2.5° regular grid, the number of grid points exceeds 10 000. Thus, even moderate resolution requires solving a high-dimensional eigenvalue problem. However, in practice, we typically are interested only in the leading eigenvectors characterized by large length scales. Fortunately, the leading Laplacian eigenfunctions are well approximated on coarser grids. To demonstrate this, we compare Laplacian eigenfunctions computed on a 5° × 5° grid with Laplacian eigenfunctions computed on a 10° × 10° grid and then bilinearly interpolated onto a 5° × 5° grid. The pattern correlation between the first 50 Laplacian eigenfunctions in the two sets, after reordering, as discussed in the previous section, is shown in Fig. 3. The pattern correlations exceed 0.96, with most exceeding 0.99. These high pattern correlations indicate that the leading Laplacian eigenfunctions computed on coarser grids and then interpolated onto finer grids are practically the same as the leading Laplacian eigenfunctions computed on the finer grid.

*M*Laplacian eigenfunctions computed on the fine grid be the column vectors of the matrix

*M*leading Laplacian eigenfunctions is

*M*columns of

Another potential concern is that numerical problems may be encountered when the grid is high resolution, especially if the grid includes points close to the poles (recall that pole points themselves are excluded from the grid). In particular, the rows and columns of the kernel matrix (15) associated with points close to the poles become arbitrarily small as the resolution increases. However, our experiments with high-resolution grids localized near the poles reveal that the eigenfunctions are remarkably insensitive to resolution, suggesting that this ill conditioning is not a problem in practice. This robustness is perhaps a consequence of the fact that when a row and column of a matrix approaches zero, the resulting eigenvalues also approach zero. Importantly, eigenvalues near zero correspond to high wavenumbers because of the inverse relation between eigenvalues of (15) and wavenumber. Thus, this singularity probably influences mostly the high wavenumber eigenfunctions, which typically are ignored. Technically, the matrix

An alternative approach to finding the leading Laplacian eigenfunctions on a fine grid is to apply iterative techniques like Rayleigh quotient iteration or inverse iteration. However, these techniques work best for sparse matrices, which

Finally, the above Laplacian eigenfunctions depend on the discretization. This dependence is a necessary part of orthogonality; vectors that are orthogonal on one grid are not orthogonal when interpolated onto another grid. The results shown in Figs. 3 and 4, which involve dramatic changes in grid, suggest that the leading Laplacian eigenfunctions are not sensitive to discretization.

## 5. Issues related to the geometry of the boundary conditions

The properties of Laplacian eigenfunctions in bounded domains have received considerable attention in various disciplines, including mathematics, physics, and computational sciences [see Grebenkov and Nguyen (2013) for an extensive review]. In this section, we discuss additional aspects of the proposed method, some of which raise difficult mathematical questions.

### a. Connectedness

Standard Laplacian eigenvalue problems assume that the domain geometry is connected; that is, the domain cannot be broken into disconnected spaces. In contrast, climate studies often involve disconnected domains, such as a land domain comprising isolated continents and islands. If the domain comprises disconnected spaces, then eigenfunctions satisfying Dirichlet or Neumann boundary conditions are uncoupled and can be solved separately for each domain. In contrast, applying the above Green’s function method to a set of disconnected spaces results in eigenfunctions in which the spaces are coupled. That is, each eigenfunction has nonzero amplitude in each geographically disconnected domain. The reason for this coupling is that the implicit boundary conditions are nonlocal and impose long-range connections between disconnected spaces. If desired, these long-range connections can be broken simply by setting to zero those matrix elements in the Green’s function corresponding to impulses and responses in disconnected spaces. Whether the desired eigenfunctions should in fact be coupled or uncoupled is a choice that depends on application. In the examples shown in section 7, we derive coupled eigenfunctions for the land domain, which itself is disconnected because of islands and continents.

### b. Appropriateness of the boundary condition

Laplacian eigenfunctions depend on the boundary conditions. For instance, if Neumann boundary conditions are imposed, then the resulting eigenfunctions have vanishing normal gradients at the boundary and therefore cannot describe functions with nonvanishing normal gradients at the boundary. The question arises as to whether the utility of the eigenfunctions is diminished if the climate variable under study does satisfy the assumed boundary conditions?

First, note that the eigenfunctions produced by the discrete method are complete. This is clear from the fact that they are the eigenvectors of a square, symmetric matrix. When the eigenfunctions are used to represent a function that does not satisfy the boundary conditions, spurious oscillations tend to occur near the boundaries (e.g., Gibbs phenomena). In the continuous problem, these oscillations become increasingly concentrated near the boundaries as more eigenfunctions are added together but always exist in any finite series representation. In the discrete problem, these oscillations similarly become concentrated near the boundaries but eventually become concentrated into a space smaller than the grid size, so that they eventually disappear in the finite dimensional problem when the complete set of eigenfunctions are used.

Whether the boundary conditions imposed by the method are realistic is unknown. This question can be answered only empirically in the context of specific datasets. Our experience suggests that the implied boundary conditions tend to produce physically appealing eigenfunctions in a wide variety of geometries. Note that incompatible boundary conditions is not problematic in data-driven methods like principal component analysis because the basis vectors are obtained by linear projection and hence inherit whatever (linear) boundary conditions are satisfied by the data.

### c. Smoothness

If the boundary is irregular or fractal, then a new phenomenon known as localization can occur (Grebenkov and Nguyen 2013). Localization refers to the tendency for the amplitude of the eigenfunction to be concentrated in a small subset of the domain, often near a boundary. Thus, as the domain boundary becomes increasingly irregular, especially at fractal-like coastlines, the Laplacian eigenfunctions may exhibit sensitive and complicated behavior. In particular, if the eigenfunctions are sensitive to the structure of a particular domain boundary, then the eigenfunctions may have limited usefulness as a basis set for representing data in that domain. Unfortunately, a rigorous theory of such phenomena is lacking. Fortunately, we have not found this behavior to be problematic, as will be shown shortly, but it is a possibility of which to be aware.

## 6. Data

We illustrate the usefulness of Laplacian eigenfunctions by using them to study variability of surface temperature and precipitation. For climate models, we use historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Taylor et al. 2012). Historical simulations refer to climate model runs that simulate the period 1880–2000 forced by anthropogenic and natural forcings. We project Laplacian eigenfunctions on monthly mean atmospheric 2-m temperature and precipitation (the variables “tas” and “pr” in the CMIP5 protocol) and sea surface temperatures (“tos”). All available models were included in this analysis.

To validate model simulations, simulated 2-m temperatures over land are compared to the observational estimates of Fan and van den Dool (2008). These estimates are monthly means starting from 1948 and derived from land station data from the Global Historical Climatology Network, version 2 (GHCNv2), and the Climate Anomaly Monitoring System (CAMS). Over the ocean, sea surface temperature is compared to observational estimates from the Extended Reconstructed Sea Surface Temperature (ERSST), version 3b (Smith et al. 2008). These estimates start from 1854 and are derived from the International Comprehensive Ocean–Atmosphere Data Set. Simulated precipitation is compared with observational estimates derived from NOAA’s Precipitation Reconstruction over Land (PREC/L) (Chen et al. 2002). These estimates are monthly means starting from 1948 and derived from gauge observations over land from GHCNv2 and CAMS.

All datasets were interpolated onto a common 5° × 5° regular grid and then projected onto the Laplacian eigenfunctions calculated from the Green’s function method.

## 7. Comparison between observations and climate models

### a. Land

The first nine Laplacian eigenfunctions over global land are shown in Fig. 5. The first Laplacian eigenfunction obviously corresponds to the land average temperature. The next two correspond mostly to a Eurasian–South American gradient and to a North American–African gradient. Note that the eigenfunctions are smooth and exhibit no localization near jagged coastlines. Moreover, the patterns are global even though some continents are disconnected.

The projection coefficients are computed for the period 1950–2000 without removing the mean climatology. The mean and standard error (i.e., mean value plus and minus one standard deviation) of the amplitudes of the first three Laplacian eigenfunctions for the CMIP5 models for four selected months are shown in Fig. 6. The gray lines show the mean plus or minus the standard error of observations. The figure shows that the variability of average land temperature is inconsistent with observations for about a third of the CMIP5 models (as indicated by the fact that the standard error of these models do not overlap with the standard error of the observations). Also, HadGEM2 falls several standard deviations from the observed mean in some months but not in other months, indicating a significant error in the annual cycle that cannot be corrected by a single bias correction. Interestingly, the dispersion about the mean is fairly consistent across models and fairly consistent with observations. The trend of the projection coefficients during the 26-yr period 1975–2000 are shown in Fig. 7. The trend for the full 51-yr period 1950–2000 turns out to be insignificant owing to the cooling during 1950–70 that offset the warming after 1970; model trends are consistent with each other and with observations for the 1950–2000 period for the first three Laplacian eigenfunctions. For the first eigenfunction, observed 2-m temperature has a significant trend in all four months, and model trends are consistent with observed trends (as indicated by the fact that the confidence intervals for models overlap with those for observations). For the second eigenfunction, significant positive trends are found in observations only for July and October, but a few models give significant negative trends. For the third eigenfunction, no significant trends are found for observations, and all but one model is consistent with observed trends. In general, the vast majority of model trends of 2-m land temperature are consistent with observed trends for all three Laplacian eigenfunctions.

Amplitudes also were computed for land precipitation. The mean value plus and minus one standard deviation of the amplitudes of the Laplacian eigenfunctions for the CMIP5 models for four selected months are shown in Fig. 8. More than half the models are seen to be inconsistent with observations for all three Laplacian eigenfunctions. Models disagree significantly even with the sign of the mean for the second and third eigenfunctions. Note also that the dispersion about the mean is similar between models and fairly consistent with observations for most models. Trends also were computed, but the trends are insignificant and model trends are consistent with observations.

### b. Ocean

The first nine Laplacian eigenfunctions over the ocean are shown in Fig. 9. As expected, the first eigenfunction corresponds to the ocean mean. The next two Laplacian eigenfunctions correspond to mostly an Atlantic–Pacific gradient and to an Indian–American gradient. The mean and standard error of the amplitudes of Laplacian eigenfunctions 2–4 are shown in Fig. 10. Results for the first eigenfunction are not shown because they essentially reproduce the well-known fact that models have large biases in global average temperature (e.g., Flato et al. 2013). The figure reveals considerable disagreement between models, and between model and observations, for all three Laplacian eigenfunctions. Some models are several standard deviations from observations. No model is consistent with observations in all 4 months for the second eigenfunction. Some models disagree even with the sign of the third eigenfunction. Most models are consistent with observed trends for all three Laplacian eigenfunctions (not shown). The fourth eigenfunction over the ocean is of interest because it corresponds to the north–south hemispheric gradient. The mean and standard error of the amplitudes, shown in the bottom panel of Fig. 10, reveals major inconsistencies between models and between model and observations. Surprisingly, some models disagree even with the sign of the pattern in some months.

### c. Variance

The percent of variance explained by the Laplacian eigenfunctions is shown in Fig. 11. The thin gray lines show variances in CMIP5 historical simulations, while the line with dots shows variances in observations. Only results for the month of June are shown, but results for other months support the same general conclusion: climate models tend to accumulate variance faster than observations for land temperature, and climate models are most consistent with observations for land precipitation. The variance spectrum for land precipitation is approximately constant (“flat”), so the explained variance increases approximately linearly.

## 8. Summary and discussion

This paper proposed a new method for representing climate data based on eigenfunctions of the Laplace operator over an arbitrary domain on a sphere. The eigenfunctions were computed using an approach suggested by Saito (2008) but specialized to domains on a sphere and modified to include the spatially uniform pattern and to account for singularities more systematically. The resulting eigenfunctions satisfy a nonlocal boundary condition that differs from the traditional Dirichlet and Neumann boundary conditions of mathematical physics, but this difference is secondary for studies that use these functions to define an orthogonal basis set for representing data rather than to solve Laplace’s equation directly. Happily, the nonlocal boundary condition is implicit in the method and does not appear explicitly in the method. The resulting eigenfunctions are orthogonal with respect to an area-weighted inner product and can be ordered by a measure of length scale. As a result, these patterns provide an objective, intuitive basis for filtering small-scale variability. These patterns depend only on the geometry of the domain and hence do not require training data from models or observations. Potentially, this method may have significant advantages in studies with small sample sizes compared to data-based pattern methods (e.g., principal component analysis or singular value decomposition).

Computation of the above Laplacian eigenfunctions is relatively straightforward. First, one defines the spatial domain of interest. The domain can be multiply connected, defined on an irregular grid, or interrupted by “missing grid points.” In particular, the domain can be defined for just land, just ocean, or selected subsets of each. Second, one constructs the matrix

An interesting property of the eigenfunctions can be seen when they are derived for a domain comprising disconnected domains. In the case of traditional Dirichlet or Neumann boundary conditions, Laplacian eigenfunctions of disconnected domains are uncoupled and can be solved separately. In contrast, the procedure in this paper produces Laplacian eigenfunctions that have nonzero amplitudes in each of the disconnected domains; that is, the domains are coupled together. This coupling arises from the (implicit) nonlocal boundary conditions imposed in the procedure but can be “turned off,” if desired, by zeroing out appropriate elements of the matrix

The above methodology was applied to monthly mean temperature and precipitation in CMIP5 simulations and in observations. The first eigenfunction is always the spatially uniform pattern. The amplitude of this pattern is obtained by performing an area average of the data over the domain. Such averages are common for defining climate indices; for example, the global average temperature is a key indicator of climate change. The secondary Laplacian eigenfunctions receive much less attention but seem no less important to understand and simulate correctly. As has been found in previous studies, the comparison revealed significant model biases in the uniform pattern for temperature and precipitation. The present study extends this result by showing significant model biases in the secondary Laplacian eigenfunctions as well, with some models constituting “outliers” and disagreeing even with the sign of the pattern. Models are generally consistent with observed trends, although some models are clear outliers in certain cases. The fourth Laplacian eigenfunction over the ocean, which measures the north–south hemispheric gradient, is not consistent across models nor consistent between models and observations, with some models disagreeing even with the sign of this pattern.

MATLAB and R codes for deriving Laplacian eigenfunctions are available upon request.

## Acknowledgments

We thank Dimitris Giannakis for insightful comments that led to significant clarifications in this paper. Discussions with Naoki Saito, Dimitris Giannakis, Robert Sachs, and Tim Sauer were extremely helpful in the development of this work. We also thank two anonymous reviewers for helpful comments. This research was supported primarily by the National Oceanic and Atmospheric Administration, under the Climate Test Bed program (NA10OAR4310264). Additional support was provided by the National Science Foundation (NSF 1338427), National Aeronautics and Space Administration (NNX14AM19G), and the National Oceanic and Atmospheric Administration (NA14OAR4310160). The views expressed herein are those of the authors and do not necessarily reflect the views of these agencies.

## REFERENCES

Allen, M. R., and S. F. B. Tett, 1999: Checking for model consistency in optimal fingerprinting.

,*Climate Dyn.***15**, 419–434, doi:10.1007/s003820050291.Belkin, M., and P. Niyogi, 2003: Laplacian eigenmaps for dimensionality reduction and data representation.

,*Neural Comput.***15**, 1373–1396, doi:10.1162/089976603321780317.Chen, M., P. Xie, J. E. Janowiak, and P. A. Arkin, 2002: Global land precipitation: A 50-year monthly analysis based on gauge observations.

,*J. Hydrometeor.***3**, 249–266, doi:10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2.Coifman, R. R., and S. Lafon, 2006: Diffusion maps.

,*Appl. Comput. Harmonic Anal.***21**, 5–30, doi:10.1016/j.acha.2006.04.006.Courant, R., and D. Hilbert, 1962:

Vol. 1. John Wiley and Sons, 560 pp.*Methods of Mathematical Physics.*Fan, Y., and H. van den Dool, 2008: A global monthly land surface air temperature analysis for 1948–present.

,*J. Geophys. Res.***113**, D01103, doi:10.1029/2007JD008470.Farrell, B. F., and P. J. Ioannou, 2001: Accurate low-dimensional approximation of the linear dynamics of fluid flow.

,*J. Atmos. Sci.***58**, 2771–2789, doi:10.1175/1520-0469(2001)058<2771:ALDAOT>2.0.CO;2.Flato, G., and Coauthors, 2013: Evaluation of climate models.

*Climate Change 2013: The Physical Science Basis*, T. F. Stocker et al., Eds., Cambridge University Press, 741–866.Fogelson, A., and J. Keener, 2001: Immersed interface methods for Neumann and related problems in two and three dimensions.

,*SIAM J. Sci. Comput.***22**, 1630–1654, doi:10.1137/S1064827597327541.Giannakis, D., and A. J. Majda, 2012: Nonlinear Laplacian spectral analysis for time series with intermittency and low-frequency variability.

,*Proc. Natl. Acad. Sci. USA***109**, 2222–2227, doi:10.1073/pnas.1118984109.Grebenkov, D. S., and B.-T. Nguyen, 2013: Geometrical structure of Laplacian eigenfunctions.

,*SIAM Rev.***55**, 601–667, doi:10.1137/120880173.Lawley, D. N., 1956: Tests of significance for the latent roots of covariance and correlation matrices.

,*Biometrika***43**, 128–136, doi:10.1093/biomet/43.1-2.128.Mardia, K. V., J. T. Kent, and J. M. Bibby, 1979:

Academic Press, 518 pp.*Multivariate Analysis.*Nadler, B., S. Lafon, R. R. Coifman, and I. G. Kevrekidis, 2006: Diffusion maps, spectral clustering and reaction coordinates of dynamical systems.

,*Appl. Comput. Harmonic Anal.***21**, 113–127, doi:10.1016/j.acha.2005.07.004.Ropelewski, C., and M. Halpert, 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation.

,*Mon. Wea. Rev.***115**, 1606–1626, doi:10.1175/1520-0493(1987)115<1606:GARSPP>2.0.CO;2.Saito, N., 2008: Data analysis and representation on a general domain using eigenfunctions of Laplacian.

,*Appl. Comput. Harmonic Anal.***25**, 68–97, doi:10.1016/j.acha.2007.09.005.Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements to NOAA’s historical merged land–ocean surface temperature analysis (1880–2006).

,*J. Climate***21**, 2283–2296, doi:10.1175/2007JCLI2100.1.Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experimental design.

,*Bull. Amer. Meteor. Soc.***93**, 485–498, doi:10.1175/BAMS-D-11-00094.1.