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Abstract The ocean removes man-made (anthropogenic) carbon from the atmosphere and thereby mitigates climate change. Observations from global hydrographic surveys reveal the spatial and temporal evolution of the ocean inventory of anthropogenic carbon and suggest substantial decadal variability in historical storage rates. Here, we use a 100-member ensemble of an Earth system model to investigate the influence of external forcing and internal climate variability on historical changes in ocean anthropogenic carbon storage over 1994 to 2014. Our findings reveal that the externally forced, decadal changes in storage are largest in the Atlantic (2–4 mmol m⁻³decade⁻¹) and positive nearly everywhere. Internal climate variability modulates regional ocean anthropogenic carbon storage trends by up to 10 mmol m⁻³decade⁻¹. The influence of internal climate variability on decadal storage changes is most prominent at depths of ∼300 m and at the edges of the subtropical gyres. Internal variability in anthropogenic carbon in the extratropics has high spectral power on decadal to multi-decadal timescales, indicating that the approximately decadal repetitions of hydrographic surveys may produce storage change estimates that are heavily influenced by internal climate variability. 

Abstract Phytoplankton in the Arctic Ocean and sub‐Arctic seas support a rich marine food web that sustains Indigenous communities as well as some of the world's largest fisheries. As sea ice retreat leads to further expansion of these fisheries, there is growing need for predictions of phytoplankton net primary production (NPP), which will likely allow better management of food resources in the region. Here, we use perfect model simulations of the Community Earth System Model version 2 (CESM2) to quantify short‐term (month to 2 years) predictability of Arctic Ocean NPP. Our results indicate that NPP is potentially predictable during the most productive summer months for at least 2 years, largely due to the highly predictable Arctic shelves where fisheries in the Arctic are projected to expand. Sea surface temperatures, which are an important limitation on phytoplankton growth and also are predictable for multiple years, are the most important physical driver of this predictability. Finally, we find that the predictability of NPP in the 2030s is enhanced relative to the 2010s, indicating that the utility of these predictions may increase in the near future. This work indicates that operational forecasts using Earth system models may provide moderately skillful predictions of NPP in the Arctic, possibly aiding in the management of Arctic marine resources. 

Abstract Population ecology and biogeography applications often necessitate the transfer of models across spatial and/or temporal dimensions to make predictions outside the bounds of the data used for model fitting. However, ecological data are often spatiotemporally unbalanced such that the spatial or the temporal dimension tends to contain more data than the other. This unbalance frequently leads model transfers to become substitutions, which are predictions to a different dimension than the predictive model was built on. Despite the prevalence of substitutions in ecology, studies validating their performance and their underlying assumptions are scarce.Here, we present a case study demonstrating both space‐for‐time and time‐for‐space substitutions (TFSS) using emperor penguins (Aptenodytes forsteri) as the focal species. Using an abundance‐based species distribution model (aSDM) of adult emperor penguins in attendance during spring across 50 colonies, we predict long‐term annual fluctuations in fledgling abundance and breeding success at a single colony, Pointe Géologie. Subsequently, we construct statistical models from time series of extended counts on Pointe Géologie to predict average colony abundance distribution across 50 colonies.Our analysis reveals that the distance to nearest open water (NOW) exhibits the strongest association with both temporal and spatial data. Space‐for‐time substitution performance of the aSDM, as measured by the Pearson correlation coefficient, was 0.63 and 0.56 when predicting breeding success and fledgling abundance time series, respectively. Linear regression of fledgling abundance on NOW yields similar TFSS performance when predicting the abundance distribution of emperor penguin colonies with a correlation coefficient of 0.58.We posit that such space–time equivalence arises because: (1) emperor penguin colonies conform to their existing fundamental niche; (2) there is not yet any environmental novelty when comparing the spatial versus temporal variation of distance to the nearest open water; and (3) models of more specific components of life histories, such as fledgling abundance, rather than total population abundance, are more transferable. Identifying these conditions empirically can enhance the qualitative validation of substitutions in cases where direct validation data are lacking.