2.1. Convective Processes

Convection is heat and moisture transfer due to a current created by hot air rising and cold air sinking. Convection is a key process in the formation of clouds that (1) produces extreme precipitation, tornados, and so on, and (2) determines the magnitude of atmospheric warming in response to rising greenhouse gas concentrations. Scientists and decision makers rely on climate models for predictive insight concerning future climate change, particularly extreme events like droughts, tornadoes, and monsoons. However, many physical processes that are key to accurately predicting extreme events are not well understood and occur on scales much smaller than the model’s resolution. But modeling at a convective-permitting resolution at the global scale is computationally intensive. As a result, such processes are simplified and approximated in indirect representations called parameterizations.

2.2. Traditional Parameterization

Parameterization is a method of indirectly representing such processes using simplified mathematical expressions. These expressions describe the effects of the process on the rest of the model as a function of variables that are directly represented in the model. The parameterization takes the state of the grid box as an input and calculates how large-scale variables evolve over time. A necessary condition for parameterization is the closure assumption, or the hypothesized existence of a physical relationship between the resolved large-scale variable and subgrid processes. This relationship facilitates the identification of appropriate large-scale variables with which scientists can indirectly represent the subgrid processes. Closure assumptions can be independently and empirically constrained by observational studies. For example, a common closure assumption is that cumulus convection occurs only if the environment is conditionally unstable (Betts and Miller Reference Betts and Miller1984).

However, observational studies like Thompson et al. (Reference Thompson, Payne, Recker and Reed1979) demonstrated that instability was inversely related to convective activity. Thus, observational studies challenged the soundness of this assumption and decreased confidence in the projections of models that incorporated this assumption. This is one example of how traditional parameterizations incorporate physical and empirical knowledge to effectively constrain model projections.

2.3. Parametric Uncertainty

Nevertheless, the indirect representation of climate processes in traditional parameterization does not provide a strong enough constraint on model predictions. The parameterization of cloud-related processes, particularly convection, is the primary driver of intermodel disagreement and the source of the largest uncertainty for model predictions of climate change (Bony et al. Reference Bony2015). This is due to parametric uncertainty concerning the best parameter value for a model.

An important difference between various parameterization schemes of a climate process is the estimation of the parameter values associated with the large-scale variables used to indirectly represent subgrid processes. Such parameter values are poorly constrained by first principles or observational data. A common practice is to tune a climate model, whereby a scientist estimates or makes ad hoc changes to the values of uncertain parameters to improve the statistical fit between model results and observational data. Given the large set of parameters required to start up and run a complex model like a GCM, this parametric uncertainty is compounded. This is significant given that parametric uncertainty concerning ideal parameter values gives rise to uncertainty about model predictions and behavior.

Scientific practices, like tuning, can limit the value of using model fit assessments to build confidence in model predictions. Model fit assessment, assessing the statistical fit of model results to observational data, is a poor indication of the model’s ability to adequately represent key processes and predict the observed values of variables related to those processes since the model was deliberately tuned to achieve fit to observational data. Extensively tuning a model’s parameters to fit past data undercuts the model’s skill in making reliable predictions about a future climate that is different from the past and present climate to which the model parameters were tuned.

Traditional parameterization methods face challenges from parametric uncertainty and widespread practices like parameter tuning. Both challenges are rooted in the indirect representation of key processes that provides a weaker-than-needed constraint on model projections. These limitations give rise to the development of cloud-resolving models (CRMs).

4.1. Artificial Neural Networks

Neural networks have been successfully used in a wide array of applications such as image recognition, self-driving cars, and so on. Given a large body of data, neural networks are trained to describe the evolution of nonlinear processes. Many climate processes are nonlinear, and different components of the climate system interact in a nonlinear way. Because of the immense complexity of the climate system and the technical limitations of computing power, it is necessary to approximate the nonlinear functions that describe climate processes. ANNs have a property, the universal approximation function, that enables the ANN to approximate any nonlinear deterministic function. This property holds irrespective of the character of the application (Earth’s climate system) or limited knowledge of underlying processes (cloud-related processes) that may pose a challenge for the development of a clear physically based algorithm (Schmidhuber Reference Schmidhuber2015). Thus, ANNs are a promising method by which scientists can better incorporate cloud-related processes, like convection, which cannot be adequately represented by physical equations in GCM.

4.2. Neural Network Parameterization

Scientists are training an ANN to learn the improved representation of convective clouds from a CRM. This involves fitting a statistical model to the output data of a CRM. The ML model predictions are then repeatedly compared to the CRM predictions with the aim of minimizing error between the ML model output and CRM output. Thus, the ML model learns the mapping between the input and output variables from the CRM, without being explicitly programmed. The trained neural network then replaces the traditional parameterization in a GCM (now a hybrid GCM) and interacts with other parts of the model.

Climate scientists check for three things when testing the NNP: whether the hybrid GCM with the NNP can accurately simulate (1) basic climate statistics, (2) patterns of climate variability and extreme events, and (3) climate change. The ability to simulate climate change is a test of the generalizability of the NNP beyond the training data. Does the hybrid GCM exhibit model fit for climate statistics and variability because the NNP memorized the CRM data or because it learned some basic physical relations underlying the CRM output data and driving climate change from the CRM? This is important because one of the aims of developing ML parameterizations is to generate insight into physical processes from large climate data sets (Monteleoni et al. Reference Monteleoni, Schmidt, Alexander, Niculescu-Mizil, Steinhaeuser, Tippett, Banerjee, Blumenthal, Ganguly, Smerdon, Tedesco, Yu, Chawla and Simoff2013; Ganguly et al. Reference Ganguly, Kodra, Agrawal, Banerjee, Boriah, Chatterjee, Chatterjee, Choudhary, Das and Faghmous2014).

Example: Rasp et al. (2018).—Rasp et al. (Reference Rasp, Pritchard and Gentine2018) train a nine-layer-deep neural network to learn atmospheric subgrid processes from a multiscale model that explicitly resolves convection. They use Community Atmosphere Model v3.0 (SPCAM) as the CRM. This CRM explicitly represents deep convective clouds and uses parameterizations for small-scale turbulence and cloud microphysics. The CRM is embedded in each grid column of the GCM. The embedded CRM outputs predictions of the subgrid tendencies of climate variables like humidity as a function of the atmospheric state at each grid column and for every time step. A neural network is trained on a year’s worth of CRM output data for these variables and generates its own predictions of the output variables. It is tested to see whether it can learn from the explicitly represented convection in SPCAM.

Rasp et al. then replace a traditional parameterization in a GCM with the trained neural network. The neural network version of CAM is called Neural Network Community Atmosphere Model (NNCAM), and they run simulations for 5 and 50 years. A key test for Rasp et al. is whether the neural network can learn from SPCAM to avoid the shortcomings characteristic of traditional parameterizations, such as a double ITCZ (a precipitation bias). They then run a stable multiyear predictive simulation using the hybrid GCM with the trained neural network.

5.1. Failure of Generalizability

When scientists speak of ML models competing or beating physically based climate models, they generally mean in terms of predictive performance. It is widely accepted that ML models, neural networks in particular, are highly predictive and generalize very well but at the expense of being “black boxes” or opaque to scientific understanding and physical interpretation (McGovern et al. Reference McGovern, Lagerquist, Gagne, Jergensen, Elmore, Homeyer and Smith2019).

Generalizability is a question of how well the ML model learns by testing its predictive skill in new situations. With respect to Rasp et al.’s (Reference Rasp, Pritchard and Gentine2018) study, can NNCAM accurately simulate climate and learn the effects of unprecedented forcing levels, like high sea surface temperatures of 4 K, which are not seen in the training data? Can neural nets learn the effects of extremely high CO₂ levels or predict climate system behavior in areas not included in the training data set?

Unfortunately, ML parameterizations fail to generalize. Despite Rasp et al.’s claims that “NNCAM’s ability … represents a major advantage compared with traditional parameterizations” (Reference Rasp, Pritchard and Gentine2018, 9687), NNCAM fails to generalize outside the training data set. The failure in generalizability is a significant blow to the utility of NNPs in GCMs for climate model prediction.

5.2. NNCAM Failure and Beyond

Despite initially positive results, the hybrid GCM (NNCAM) generalizes poorly to out-of-sample temperatures. NNCAM undergoes three sensitivity tests with perturbed sea surface temperatures to assess the parameterization’s generalizability outside the range of its training data. NNCAM is able to run stably with sea surface temperature perturbations up to 3 K. However, when the perturbations have an amplitude of 4 K, the NNCAM is unable to generalize: “the ITCZ signal is washed out and unrealistic patterns develop in and above the boundary layer. … As a result the temperature bias is significant, particularly in the stratosphere” (Rasp et al. Reference Rasp, Pritchard and Gentine2018, 9688). For these reasons, Rasp et al. conclude that “the neural network cannot handle temperatures that exceed the ones seen during training” (9687). Rasp et al. attribute this failure of generalizability to the traditional problem of overfitting in ML (9688).

This failure in generalizability is a recurring problem for ML parameterization models (see table 1).Footnote ² The consistent failure to generalize is problematic on two fronts. First, the goal of adopting ML methods in climate modeling was to leverage the advantages of CRMs for global climate prediction. Inadequate predictive skill is a failure to fulfill this primary aim. Second, neural networks are often presumed to be highly predictive but not explanatory. Much attention has been paid to opening the “black box” of neural networks and the challenges neural networks pose for understanding and explanation (McGovern et al. Reference McGovern, Lagerquist, Gagne, Jergensen, Elmore, Homeyer and Smith2019). However, surprisingly, prediction is also a challenge for neural networks in climate science applications. In the next section, I offer an explanation that relates NNPs’ lack of process representation to their predictive inadequacy.

Table 1.

Studies That Investigate Machine Learning Convective Parameterizations and Their Generalizability

Study	Does It Generalize?
Rasp, Pritchard, and Gentine (Reference Rasp, Pritchard and Gentine2018)	No
O’Gorman and Dwyer (Reference O’Gorman and Dwyer2018)	No
Scher and Messori (Reference Scher and Messori2019)	No
Yuval and O’Gorman (Reference Yuval and O’Gorman2020)	No

6.1. From Tuning to Training and Back Again

The original concern with using model fit assessment to support a model’s adequacy for purpose was that model performance may be due to ad hoc tuning rather than a model’s accurate representation or prediction of climate processes. This concern is exacerbated with neural network models that are trained on a CRM whose parameters have been individually tuned to observational data and that may still incorporate parameterized components.

For example, the CRM in the Rasp et al. (Reference Rasp, Pritchard and Gentine2018) study—SPCAM—still parameterizes cloud microphysics and small-scale turbulence. The CRM includes processes that are directly represented and others that are parameterized because they are not well understood. The values of those parameters are estimated to maximize the statistical fit of the CRM’s output with the observational data. The ANN is then trained on the output data of the previously tuned CRM. NNP predictions are then repeatedly compared to the CRM output with the aim of minimizing error between the NNP predictions and CRM output (see fig. 1). This can be summarized, as follows.

1. 1. P is a key parameter in a CRM. Scientists may tune the parameter, or estimate the value of P, to maximize the statistical fit between the observational data and the CRM output.

2. 2. A neural network is trained on the output data of the CRM, which are calculated from the CRM as a whole including P.

3. 3. NNP predictions are repeatedly compared to the CRM outputs with the aim of minimizing the statistical distance between the CRM and NNP predictions.

This means neural network predictions may fit with CRM predictions for any number of reasons: (1) chance, (2) adequacy, (3) tuning/overfitting, or (4) some combination of these.

Figure 1.

Visual representation of the cascade of uncertainty in neural network parameterizations due to model tuning and training practices.

Training ML parameterizations on previously tuned CRMs has two consequences. First, it is more challenging to identify how model performance depends on tuning and training procedures. Second, it diminishes the epistemic import of model fit assessment, the only form of model evaluation available. It is more likely that there would be good model fit whether the model is or is not adequately predictive out of sample because the purpose of tuning and training is to optimize model fit to observational and CRM output data, respectively. The iterative training of an NNP on a previously tuned CRM makes 3 the most likely explanation for model fit.

6.2. Potential Objection

Some may object that this concern is overblown. As long as the variables assessed for model fit are distinct from the parameters subject to tuning methods, then the epistemic value of good model fit can be upheld. In fact, the CRM parameters most likely to be tuned in an ad hoc manner are, for example, cloud microphysics parameters, not variables associated with directly represented convective clouds or large-scale variables like temperature. When the NNP is trained on the CRM, it is being evaluated for model fit with respect to mean climate statistics like mean temperature. This independence of the variables tuned and variables evaluated for fit ought to dispel worries about the dependence of fit or performance on tuning practices.

However, in the development of the NNP, we are concerned not just with tuning but with tuning coupled with training practices. The independence is no longer that of just the variable assessed for fit and the parameter tuned. Scientists now face the threefold challenge of distinguishing whether performance depends on tuning, training, or the compounded effects of training a model on a previously tuned CRM. Complex dependencies arise out of practices and interactions that span three related contexts: observational data, the CRM, and the neural network model. Put simply, epistemic opacity is deepened in more than one way (Alvarado and Humphreys Reference Alvarado and Humphreys2017).

Furthermore, we know that the neural network model is trained on CRM data concerning the same climate variables for which it is subsequently evaluated using model fit assessment. For example, in Rasp et al. (Reference Rasp, Pritchard and Gentine2018), NNCAM is trained on 140 million training samples from SPCAM, about a year’s worth of training data, for temperature and wind profiles. Rasp et al. then evaluate NNCAM’s ability to reproduce SPCAM’s climate with respect to temperature and wind profiles (9685). Neural networks improve performance via training methods whose efficacy relies on a dependence between the variables used in training and those evaluated for fit with the CRM output data. Training improves performance because it exploits exactly the type of dependence that scientists need to rule out in order for model fit evaluations to have epistemic value. Otherwise, scientists risk overtuning or overfitting to past or recent conditions, and “the model’s predictive accuracy might well deteriorate as GMST projections are made for farther and farther into the future” (Parker Reference Parker2009, 239)—a failure in generalizability.

6.3. Significance

This is a significant concern since the evaluation of the NNP centers on model fit assessment. Rasp et al. assess the hybrid GCMs simulation of (1) key climate statistics like mean climate and climate variability, (2) properties like energy conservation, and (3) the degree to which the hybrid GCM can generalize outside the training data. They do so by comparing NNCAM’s ability to reproduce SPCAM’s climate, a form of model fit assessment whose value relies on the improved statistical fit of SPCAM to observational data relative to the traditional parameterization. Rasp et al. find that the hybrid GCM, NNCAM, successfully reproduces important aspects of the SPCAM training model’s mean climate, key patterns of climate variability, and effectively conserves energy (Reference Rasp, Pritchard and Gentine2018, 9687). This is taken to support NNCAM’s adequacy for accurately representing and predicting the outcome of processes associated with mean climate statistics, variability, and thermodynamic principles. Rasp et al. claim that the neural network learned two things from the data set: (1) the higher-level concept of energy conservation and (2) the physical relation between input and output variables. Thus, they conclude that NNCAM’s performance “is to some extent unexpected and represents a major advantage compared with traditional parameterizations” (9687) and take their study to present a “paradigm shift” in the design of subgrid parameterizations (9688).

However, if the good model fit of NNCAM to the CRM model—whether for mean statistics, variability, or thermodynamics—is due to overfitting that particular CRM data set, then model fit is a poor test of NNCAM’s ability to learn either (1) the higher-level concept of energy conservation or (2) the physical relation between input and output variables. The failure of NNCAM’s generalizability outside the training data supports the attribution of NNCAM’s performance to the extensive tuning and training of the NNP.

Contrary to the claims of Rasp et al., the NNP has failed to learn any meaningful underlying principle or physical relations among relata. Rather, the NNP cannot make accurate or reliable predictions for temperatures that lie outside of the range to which it had been repeatedly and iteratively made to fit with by the training procedure. The limited success of the NNP with respect to the training data is due to the development and evaluation methods used in connection with the NNP, not the NNP’s having learned or captured underlying relations that give rise to those mean climate statistics, variability, and so on.

6.4. Summary

Improving and evaluating process representations are primary means of improving the reliability of model predictions and provide safeguards against overfitting through physical/empirical constraints on model predictions. This is especially valuable in the context of NNPs and other ML applications for which overfitting is a particularly trenchant challenge due to the unique coupling of tuning and training practices in the development process. But, such process-based forms of model improvement and evaluation are unavailable because NNPs do not directly or indirectly represent climate processes. This leaves scientists with model fit assessment as the only and primary form of model evaluation for NNPs. However, model fit assessment is not robust against overfitting and is of limited epistemic value due to the tuning and training methods involved in ML model development. Thus, the replacement of traditional and cloud-resolving parameterizations with NNPs undermines the reliability of model projections. Traditional and cloud-resolving parameterizations represented processes directly or indirectly, and this process representation has added an irreducible value for the reliability of model predictions because it (1) provides physical/empirical constraints and (2) facilitates forms of model development and evaluation that guard against overfitting. This is particularly important for making reliable out-of-sample predictions. The advent of ML methods, like neural networks, transforms the development and evaluation of climate model parameterizations—not always for the better.