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Abstract. Earth system models (ESMs) are useful tools forpredicting and understanding past and future aspects of the climate system.However, the biological and physical parameters used in ESMs can have widevariations in their estimates. Even small changes in these parameters canyield unexpected results without a clear explanation of how a particularoutcome was reached. The standard method for estimating ESM sensitivity isto compare spatiotemporal distributions of variables from different runs ofa single ESM. However, a potential pitfall of this method is that ESM outputcould match observational patterns because of compensating errors. Forexample, if a model predicts overly weak upwelling and low nutrientconcentrations, it might compensate for this by allowing phytoplankton tohave a high sensitivity to nutrients. Recently, we demonstrated that neuralnetwork ensembles (NNEs) are capable of extracting relationships betweenpredictor and target variables within ocean biogeochemical models. Beingable to view the relationships between variables, along with spatiotemporaldistributions, allows for a more mechanistically based examination of ESMoutputs. Here, we investigated whether we could apply NNEs to help usdetermine why different ESMs produce different spatiotemporal distributionsof phytoplankton biomass. We tested this using three cases. The first andsecond case used different runs of the same ESM, except that the physicalcirculations differed between them in the first case, while the biologicalequations differed between them in the second. Our results indicated thatthe NNEs were capable of extracting the relationships between variables fordifferent runs of a single ESM, allowing us to distinguish betweendifferences due to changes in circulation (which do not changerelationships) from changes in biogeochemical formulation (which do changerelationships). In the third case, we applied NNEs to two different ESMs.The results of the third case highlighted the capability of NNEs to contrastthe apparent relationships of different ESMs and some of the challenges itpresents. Although applied specifically to the ocean components of an ESM,our study demonstrates that Earth system modelers can use NNEs to separatethe contributions of different components of ESMs. Specifically, this allowsmodelers to compare the apparent relationships across different ESMs andobservational datasets. 

Abstract. A key challenge for biological oceanography is relating the physiologicalmechanisms controlling phytoplankton growth to the spatial distribution ofthose phytoplankton. Physiological mechanisms are often isolated by varyingone driver of growth, such as nutrient or light, in a controlled laboratorysetting producing what we call “intrinsic relationships”. We contrastthese with the “apparent relationships” which emerge in the environment inclimatological data. Although previous studies have found machine learning(ML) can find apparent relationships, there has yet to be a systematic studyexamining when and why these apparent relationships diverge from theunderlying intrinsic relationships found in the lab and how and why this may depend on the method applied. Here we conduct a proof-of-concept studywith three scenarios in which biomass is by construction a function oftime-averaged phytoplankton growth rate. In the first scenario, the inputsand outputs of the intrinsic and apparent relationships vary over thesame monthly timescales. In the second, the intrinsic relationships relateaverages of drivers that vary on hourly timescales to biomass, but theapparent relationships are sought between monthly averages of these inputsand monthly-averaged output. In the third scenario we apply ML to the outputof an actual Earth system model (ESM). Our results demonstrated that whenintrinsic and apparent relationships operate on the same spatial andtemporal timescale, neural network ensembles (NNEs) were able to extract theintrinsic relationships when only provided information about the apparentrelationships, while colimitation and its inability to extrapolate resulted in random forests (RFs) diverging from the true response. Whenintrinsic and apparent relationships operated on different timescales (aslittle separation as hourly versus daily), NNEs fed with apparentrelationships in time-averaged data produced responses with the right shapebut underestimated the biomass. This was because when the intrinsicrelationship was nonlinear, the response to a time-averaged input differedsystematically from the time-averaged response. Although the limitationsfound by NNEs were overestimated, they were able to produce more realisticshapes of the actual relationships compared to multiple linear regression.Additionally, NNEs were able to model the interactions between predictorsand their effects on biomass, allowing for a qualitative assessment of thecolimitation patterns and the nutrient causing the most limitation. Futureresearch may be able to use this type of analysis for observational datasetsand other ESMs to identify apparent relationships between biogeochemicalvariables (rather than spatiotemporal distributions only) and identifyinteractions and colimitations without having to perform (or at leastperforming fewer) growth experiments in a lab. From our study, it appearsthat ML can extract useful information from ESM output and could likely doso for observational datasets as well. 

Abstract Global climate models (GCMs) are critical tools for understanding and projecting climate variability and change, yet the performance of these models is notoriously weak over much of tropical Africa. To improve this situation, process-based studies of African climate dynamics and their representation in GCMs are required. Here, we focus on summer rainfall of eastern Africa (SREA), which is crucial to the Ethiopian Highlands and feeds the flow of the Blue Nile River. The SREA region is highly vulnerable to droughts, with El Niño–Southern Oscillation (ENSO) being a leading cause of interannual rainfall variability. Adequate understanding and accurate representation of climate features that influence regional variability is an important but often neglected issue when evaluating models. We perform a process-based evaluation of GCMs, focusing on the upper-troposphere tropical easterly jet (TEJ), which has been hypothesized to link ENSO to SREA. We find that most models have an ENSO–TEJ coupling similar to observed, but the models diverge in their representation of TEJ–SREA coupling. Differences in the latter explain the majority (80%) of variability in ENSO teleconnection simulation across the models. This is higher than the variance explained by rainfall coupling with the Somali jet (44%) and African easterly jet (55%). However, our diagnostics of the leading hypothesized mechanism in the models—variability in divergence in the TEJ exit region—are not consistent across models and suggest that a deeper understanding of the mechanisms of TEJ–precipitation coupling should be a priority for studies of climate variability and change in the region. 

Abstract Numerous climate models display large-amplitude, long-period variability associated with quasiperiodic convection in the Southern Ocean, but the mechanisms responsible for producing such oscillatory convection are poorly understood. In this paper we identify three feedbacks that help generate such oscillations within an Earth system model with a particularly regular oscillation. The first feedback involves increased (decreased) upward mixing of warm interior water to the surface, resulting in more (less) evaporation and loss of heat to the atmosphere which produces more (less) mixing. This positive feedback helps explain why temperature anomalies are not damped out by surface forcing. A second key mechanism involves convective (nonconvective) events in the Weddell Sea causing a relaxation (intensification) of westerly winds, which at some later time results in a pattern of currents that reduces (increases) the advection of freshwater out of the Weddell Sea. This allows for the surface to become lighter (denser) which in turn can dampen (trigger) convection—so that the overall feedback is a negative one with a delay—helping to produce a multidecadal oscillation time scale. The decrease (increase) in winds associated with convective (nonconvective) states also results in a decrease (increase) in the upward mixing of salt in the Eastern Weddell Sea, creating a negative (positive) salinity anomaly that propagates into the Western Weddell Sea and dampens (triggers) convection—again producing a negative feedback with a delay. A principal oscillatory pattern analysis yields a reasonable prediction for the period of oscillation. Strengths of the feedbacks are sensitive to parameterization of mesoscale eddies. 

This study examines the impact of changing the lateral diffusion coefficient A_Redion the transport of the Antarctic Circumpolar Current (ACC). The lateral diffusion coefficient A_Rediis poorly constrained, with values ranging across an order of magnitude in climate models. The ACC is difficult to accurately simulate, and there is a large spread in eastward transport in the Southern Ocean (SO) in these models. This paper examines how much of that spread can be attributed to different eddy parameterization coefficients. A coarse-resolution, fully coupled model suite was run with A_Redi= 400, 800, 1200, and 2400 m²s⁻¹. Additionally, two simulations were run with two-dimensional representations of the mixing coefficient based on satellite altimetry. Relative to the 400 m²s⁻¹case, the 2400 m²s⁻¹case exhibits 1) an 11% decrease in average wind stress from 50° to 65°S, 2) a 20% decrease in zonally averaged eastward transport in the SO, and 3) a 14% weaker transport through the Drake Passage. The decrease in transport is well explained by changes in the thermal current shear, largely due to increases in ocean density occurring on the northern side of the ACC. In intermediate waters these increases are associated with changes in the formation of intermediate waters in the North Pacific. We hypothesize that the deep increases are associated with changes in the wind stress curl allowing Antarctic Bottom Water to escape and flow northward.

 

Because colored dissolved materials (CDMs) trap incoming sunlight closer to the surface, they have the potential to affect sea surface temperatures. We compare two models, one with and one without CDMs, and show that their presence leads to an increase in the amplitude of the seasonal cycle over coastal and northern subpolar regions, which may exceed 2 °C. The size and sign of the change are controlled by the interplay between enhanced shortwave heating of the surface, shading and cooling of the subsurface, and the extent to which these are connected by vertical mixing. The changes in the seasonal cycle largely explain changes in the range of temperature extremes, an aspect of climate with important implications for ecosystem cycling. The modeled changes associated with CDMs have an intriguing resemblance to the observed trend in the annual cycle seen in recent decades, suggesting that more attention be paid to the role of “ocean yellowing” in global change.