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The Blob was the early manifestation of the Northeast Pacific marine heat wave from 2013 to 2016. While the upper ocean temperature in the Blob has been well described, the impacts on marine biogeochemistry have not been fully studied. Here, we characterize and develop understanding of Eastern North Pacific upper ocean biogeochemical properties during the Winter of 2013–2014 using in situ observations, an observation‐based product, and reconstructions from a collection of ocean models. We find that the Blob is associated with significant upper ocean biogeochemical anomalies: A 5% increase in aragonite saturation state (temporary reprieve of ocean acidification) and a 3% decrease in oxygen concentration (enhanced deoxygenation). Anomalous advection and mixing drive the aragonite saturation anomaly, while anomalous heating and air‐sea gas exchange drive the oxygen anomaly. Marine heatwaves do not necessarily serve as an analog for future change as they may enhance or mitigate long‐term trends.

 

We assess the detectability of COVID‐like emissions reductions in global atmospheric CO₂concentrations using a suite of large ensembles conducted with an Earth system model. We find a unique fingerprint of COVID in the simulated growth rate of CO₂sampled at the locations of surface measurement sites. Negative anomalies in growth rates persist from January 2020 through December 2021, reaching a maximum in February 2021. However, this fingerprint is not formally detectable unless we force the model with unrealistically large emissions reductions (2 or 4 times the observed reductions). Internal variability and carbon‐concentration feedbacks obscure the detectability of short‐term emission reductions in atmospheric CO₂. COVID‐driven changes in the simulated, column‐averaged dry air mole fractions of CO₂are eclipsed by large internal variability. Carbon‐concentration feedbacks begin to operate almost immediately after the emissions reduction; these feedbacks reduce the emissions‐driven signal in the atmosphere carbon reservoir and further confound signal detection.

 

We use a statistical emulation technique to construct synthetic ensembles of global and regional sea‐air carbon dioxide (CO₂) flux from four observation‐based products over 1985–2014. Much like ensembles of Earth system models that are constructed by perturbing their initial conditions, our synthetic ensemble members exhibit different phasing of internal variability and a common externally forced signal. Our synthetic ensembles illustrate an important role for internal variability in the temporal evolution of global and regional CO₂flux and produce a wide range of possible trends over 1990–1999 and 2000–2009. We assume a specific externally forced signal and calculate the rank of the observed trends within the distribution of statistically modeled synthetic trends during these periods. Over the decade 1990–1999, three of four observation‐based products exhibit small negative trends in globally integrated sea‐air CO₂flux (i.e., enhanced ocean CO₂absorption with time) that are within one standard deviation of the mean in their respective synthetic ensembles. Over the decade 2000–2009, however, three products show large negative trends in globally integrated sea‐air CO₂flux that have a low rate of occurrence in their synthetic ensembles. The largest positive trends in global and Southern Ocean flux over 1990–1999 and the largest negative trends over 2000–2009 fall nearly two standard deviations away from the mean in their ensembles. Our approach provides a new perspective on the important role of internal variability in sea‐air CO₂flux trends, and furthers understanding of the role of internal and external processes in driving observed sea‐air CO₂flux variability.

 

Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) have demonstrated their utility in the study of marine phytoplankton variability. These ESM large ensembles simulate the evolution of multiple alternate realities, each with a different phasing of internal climate variability. However, ESMs may not accurately represent real world variability as recorded via satellite and in situ observations of ocean chlorophyll over the past few decades. Observational records of surface ocean chlorophyll equate to a single ensemble member in the large ensemble framework, and this can cloud the interpretation of long‐term trends: are they externally forced, caused by the phasing of internal variability, or both? Here, we use a novel statistical emulation technique to place the observational record of surface ocean chlorophyll into the large ensemble framework. Much like a large initial condition ensemble generated with an ESM, the resulting synthetic ensemble represents multiple possible evolutions of ocean chlorophyll concentration, each with a different sampling of internal climate variability. We further demonstrate the validity of our statistical approach by recreating an ESM ensemble of chlorophyll using only a single ESM ensemble member. We use the synthetic ensemble to explore the interpretation of long‐term trends in the presence of internal variability and find a wider range of possible trends in chlorophyll due to the sampling of internal variability in subpolar regions than in subtropical regions.

 

The decline in global emissions of carbon dioxide due to the COVID‐19 pandemic provides a unique opportunity to investigate the sensitivity of the global carbon cycle and climate system to emissions reductions. Recent efforts to study the response to these emissions declines has not addressed their impact on the ocean, yet ocean carbon absorption is particularly susceptible to changing atmospheric carbon concentrations. Here, we use ensembles of simulations conducted with an Earth system model to explore the potential detection of COVID‐related emissions reductions in the partial pressure difference in carbon dioxide between the surface ocean and overlying atmosphere (ΔpCO₂), a quantity that is regularly measured. We find a unique fingerprint in global‐scale ΔpCO₂that is attributable to COVID, though the fingerprint is difficult to detect in individual model realizations unless we force the model with a scenario that has four times the observed emissions reduction.

 

Interannual variations in the flux of carbon dioxide (CO₂) between the land surface and the atmosphere are the dominant component of interannual variations in the atmospheric CO₂growth rate. Here, we investigate the potential to predict variations in these terrestrial carbon fluxes 1–10 years in advance using a novel set of retrospective decadal forecasts of an Earth system model. We demonstrate that globally-integrated net ecosystem production (NEP) exhibits high potential predictability for 2 years following forecast initialization. This predictability exceeds that from a persistence or uninitialized forecast conducted with the same Earth system model. The potential predictability in NEP derives mainly from high predictability in ecosystem respiration, which itself is driven by vegetation carbon and soil moisture initialization. Our findings unlock the potential to forecast the terrestrial ecosystem in a changing environment.

 

Abstract. The potential for multiyear prediction of impactful Earthsystem change remains relatively underexplored compared to shorter(subseasonal to seasonal) and longer (decadal) timescales. In this study, weintroduce a new initialized prediction system using the Community EarthSystem Model version 2 (CESM2) that is specifically designed to probepotential and actual prediction skill at lead times ranging from 1 month outto 2 years. The Seasonal-to-Multiyear Large Ensemble (SMYLE) consists of acollection of 2-year-long hindcast simulations, with four initializations peryear from 1970 to 2019 and an ensemble size of 20. A full suite of output isavailable for exploring near-term predictability of all Earth systemcomponents represented in CESM2. We show that SMYLE skill for ElNiño–Southern Oscillation is competitive with other prominent seasonalprediction systems, with correlations exceeding 0.5 beyond a lead time of 12months. A broad overview of prediction skill reveals varying degrees ofpotential for useful multiyear predictions of seasonal anomalies in theatmosphere, ocean, land, and sea ice. The SMYLE dataset, experimentaldesign, model, initial conditions, and associated analysis tools are allpublicly available, providing a foundation for research on multiyearprediction of environmental change by the wider community. 

Abstract The California Current System (CCS) sustains economically valuable fisheries and is particularly vulnerable to ocean acidification, due to its natural upwelling of carbon-enriched waters that generate corrosive conditions for local ecosystems. Here we use a novel suite of retrospective, initialized ensemble forecasts with an Earth system model (ESM) to predict the evolution of surface pH anomalies in the CCS. We show that the forecast system skillfully predicts observed surface pH variations a year in advance over a naive forecasting method, with the potential for skillful prediction up to five years in advance. Skillful predictions of surface pH are mainly derived from the initialization of dissolved inorganic carbon anomalies that are subsequently transported into the CCS. Our results demonstrate the potential for ESMs to provide skillful predictions of ocean acidification on large scales in the CCS. Initialized ESMs could also provide boundary conditions to improve high-resolution regional forecasting systems.