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Future climate projections from Earth system models (ESMs) typically focus on the timescale of this century. We use a set of five ESMs and one Earth system model of intermediate complexity (EMIC) to explore the dynamics of the Earth's climate and carbon cycles under contrasting emissions trajectories beyond this century to the year 2300. The trajectories include a very-high-emissions, unmitigated fossil-fuel-driven scenario, as well as a mitigation scenario that diverges from the first scenario after 2040 and features an "overshoot", followed by a decrease in atmospheric CO2 concentrations by means of large net negative CO2 emissions. In both scenarios and for all models considered here, the terrestrial system switches from being a net sink to either a neutral state or a net source of carbon, though for different reasons and centered in different geographic regions, depending on both the model and the scenario. The ocean carbon system remains a sink, albeit weakened by carbon cycle feedbacks, in all models under the high-emissions scenario and switches from sink to source in the overshoot scenario. The global mean temperature anomaly is generally proportional to cumulative carbon emissions, with a deviation from proportionality in the overshoot scenario that is governed by the zero emissions commitment. Additionally, 23rd century warming continues after the cessation of carbon emissions in several models in the high-emissions scenario and in one model in the overshoot scenario. While ocean carbon cycle responses qualitatively agree in both globally integrated and zonal mean dynamics in both scenarios, the land models qualitatively disagree in zonal mean dynamics, in the relative roles of vegetation and soil in driving C fluxes, in the response of the sink to CO2, and in the timing of the sink-source transition, particularly in the high-emissions scenario. The lack of agreement among land models on the mechanisms and geographic patterns of carbon cycle feedbacks, alongside the potential for lagged physical climate dynamics to cause warming long after CO2 concentrations have stabilized, points to the possibility of surprises in the climate system beyond the 21st century time horizon, even under relatively mitigated global warming scenarios, which should be taken into consideration when setting global climate policy. Copyright:

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Results from the fully and biogeochemically coupled simulations in which CO2 increases at a rate of 1%yr-1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon-concentration and carbon-climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77±0.37 ° C EgC-1 and is similar to that found in CMIP5 models (1.63±0.48 °C EgC-1) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models.

Climate change is expected to increase the frequency of flooding events and, thus, the risks of flood-related mortality and infrastructure damage. Global-scale assessments of future flooding from Earth system models based only on precipitation changes neglect important processes that occur within the land surface, particularly plant physiological responses to rising CO2. Higher CO2 can reduce stomatal conductance and transpiration, which may lead to increased soil moisture and runoff in some regions, promoting flooding even without changes in precipitation. Here we assess the relative impacts of plant physiological and radiative greenhouse effects on changes in daily runoff intensity over tropical continents using the Community Earth System Model. We find that extreme percentile rates increase significantly more than mean runoff in response to higher CO2. Plant physiological effects have a small impact on precipitation intensity but are a dominant driver of runoff intensification, contributing to one half of the 99th and one third of the 99.9th percentile runoff intensity changes.

Understanding how anthropogenic CO2 emissions will influence future precipitation is critical for sustainably managing ecosystems, particularly for drought-sensitive tropical forests. Although tropical precipitation change remains uncertain, nearly all models from the Coupled Model Intercomparison Project Phase 5 predict a strengthening zonal precipitation asymmetry by 2100, with relative increases over Asian and African tropical forests and decreases over South American forests. Here we show that the plant physiological response to increasing CO2 is a primary mechanism responsible for this pattern. Applying a simulation design in the Community Earth System Model in which CO2 increases are isolated over individual continents, we demonstrate that different circulation, moisture and stability changes arise over each continent due to declines in stomatal conductance and transpiration. The sum of local atmospheric responses over individual continents explains the pan-tropical precipitation asymmetry. Our analysis suggests that South American forests may be more vulnerable to rising CO2 than Asian or African forests.

Using the Community Earth System Model, we explore the role of human land use and land cover change (LULCC) in modifying the terrestrial carbon budget in simulations forced by Representative Concentration Pathway 8.5, extended to year 2300. Overall, conversion of land (e.g., from forest to croplands via deforestation) results in a model-estimated, cumulative carbon loss of 490 Pg C between 1850 and 2300, larger than the 230 Pg C loss of carbon caused by climate change over this same interval. The LULCC carbon loss is a combination of a direct loss at the time of conversion and an indirect loss from the reduction of potential terrestrial carbon sinks. Approximately 40% of the carbon loss associated with LULCC in the simulations arises from direct human modification of the land surface; the remaining 60% is an indirect consequence of the loss of potential natural carbon sinks. Because of the multicentury carbon cycle legacy of current land use decisions, a globally averaged amplification factor of 2.6 must be applied to 2015 land use carbon losses to adjust for indirect effects. This estimate is 30% higher when considering the carbon cycle evolution after 2100. Most of the terrestrial uptake of anthropogenic carbon in the model occurs from the influence of rising atmospheric CO2 on photosynthesis in trees, and thus, model-projected carbon feedbacks are especially sensitive to deforestation.

Version 1 of the Community Earth SystemModel, in the configurationwhere its full carbon cycle is enabled, is introduced and documented. In this configuration, the terrestrial biogeochemical model, which includes carbon-nitrogen dynamics and is present in earlier model versions, is coupled to an ocean biogeochemical model and atmospheric CO2 tracers. The authors provide a description of the model, detail how preindustrial-control and twentieth-century experiments were initialized and forced, and examine the behavior of the carbon cycle in those experiments. They examine how sea- and land-to-air CO2 fluxes contribute to the increase of atmospheric CO2 in the twentieth century, analyze how atmospheric CO2 and its surface fluxes vary on interannual time scales, including how they respond to ENSO, and describe the seasonal cycle of atmospheric CO2 and its surface fluxes. While the model broadly reproduces observed aspects of the carbon cycle, there are several notable biases, including having too large of an increase in atmospheric CO2 over the twentieth century and too small of a seasonal cycle of atmospheric CO2 in the Northern Hemisphere. The biases are related to a weak response of the carbon cycle to climatic variations on interannual and seasonal time scales and to twentieth-century anthropogenic forcings, including rising CO2, land-use change, and atmospheric deposition of nitrogen.

It takes several thousand years for the deep-ocean concentration of natural radiocarbon to come to equilibrium with surface fluxes, making it computationally too expensive to routinely simulate it with moderate- to high-resolution ocean models. We present an implicit solver for computing prebomb δ14C that requires the equivalent of only a few tens of model years to reach equilibrium. The solver uses a Newton-Krylov algorithm with a preconditioner based on a coarse-grained annually-averaged tracer-transport operator. Coarse-graining provides a general approach for developing preconditioners for models of increasing resolution. We implemented and tested the solver for the ocean component of the Community Earth System Model (CESM) with a nominal horizontal resolution of 1° × 1° and with 60 vertical levels. Simulated δ14C values are in good agreement with observations at the surface and in the North Atlantic, but the deep North Pacific simulated values show a substantial bias, with prebomb radiocarbon δ14C values translating to ages that are twice the observationally based estimate. This bias is substantially larger than published simulations obtained with coarser resolution models, suggesting that increasing model resolution does not automatically improve the fidelity of the deep ocean ventilation processes. We therefore recommend that natural δ14C be used as a deep-ocean ventilation metric for critically evaluating deep ocean circulation. © 2013 Elsevier Ltd.

The authors compare Community Earth System Model results to marine observations for the 1990s and examine climate change impacts on biogeochemistry at the end of the twenty-first century under two future scenarios (Representative Concentration Pathways RCP4.5 and RCP8.5). Late-twentieth-century seasonally varying mixed layer depths are generally within 10 m of observations, with a Southern Ocean shallow bias. Surface nutrient and chlorophyll concentrations exhibit positive biases at low latitudes and negative biases at high latitudes. The volume of the oxygen minimum zones is overestimated.The impacts of climate change on biogeochemistry have similar spatial patterns under RCP4.5 and RCP8.5, but perturbation magnitudes are larger under RCP8.5. Increasing stratification leads to weaker nutrient entrainment and decreased primary and export production (>30% over large areas). The global-scale decreases in primary and export production scale linearly with the increases in mean sea surface temperature. There are production increases in the high nitrate, low chlorophyll (HNLC) regions, driven by lateral iron inputs from adjacent areas. The increased HNLC export partially compensates for the reductions in non-HNLC waters (~25% offset). Stabilizing greenhouse gas emissions and climate by the end of this century (as in RCP4.5) will minimize the changes to nutrient cycling and primary production in the oceans. In contrast, continued increasing emission of CO2 (as in RCP8.5) will lead to reduced productivity and significant modifications to ocean circulation and biogeochemistry by the end of this century, with more drastic changes beyond the year 2100 as the climate continues to rapidly warm.

Ocean carbon uptake and storage simulated by the Community Earth System Model, version 1–Biogeochemistry [CESM1(BGC)], is described and compared to observations. Fully coupled and ocean-ice configurations are examined; both capture many aspects of the spatial structure and seasonality of surface carbon fields. Nearly ubiquitous negative biases in surface alkalinity result from the prescribed carbonate dissolution profile. The modeled sea–air CO2 fluxes match observationally based estimates over much of the ocean; significant deviations appear in the Southern Ocean. Surface ocean pCO2 is biased high in the subantarctic and low in the sea ice zone. Formation of the water masses dominating anthropogenic CO2 (Cant) uptake in the Southern Hemisphere is weak in the model, leading to significant negative biases in Cant and chlorofluorocarbon (CFC) storage at intermediate depths. Column inventories of Cant appear too high, by contrast, in the North Atlantic. In spite of the positive bias, this marks an improvement over prior versions of the model, which underestimated North Atlantic uptake. The change in behavior is attributable to a new parameterization of density-driven overflows. CESM1(BGC) provides a relatively robust representation of the ocean–carbon cycle response to climate variability. Statistical metrics of modeled interannual variability in sea–air CO2 fluxes compare reasonably well to observationally based estimates. The carbon cycle response to key modes of climate variability is basically similar in the coupled and forced ocean-ice models; however, the two differ in regional detail and in the strength of teleconnections.

Changes in atmospheric CO2 variability during the twenty-first century may provide insight about ecosystem responses to climate change and have implications for the design of carbon monitoring programs. This paper describes changes in the three-dimensional structure of atmospheric CO2 for several representative concentration pathways (RCPs 4.5 and 8.5) using the Community Earth System Model–Biogeochemistry (CESM1-BGC). CO2 simulated for the historical period was first compared to surface, aircraft, and column observations. In a second step, the evolution of spatial and temporal gradients during the twenty-first century was examined. The mean annual cycle in atmospheric CO2 was underestimated for the historical period throughout the Northern Hemisphere, suggesting that the growing season net flux in the Community Land Model (the land component of CESM) was too weak. Consistent with weak summer drawdown in Northern Hemisphere high latitudes, simulated CO2 showed correspondingly weak north–south and vertical gradients during the summer. In the simulations of the twenty-first century, CESM predicted increases in the mean annual cycle of atmospheric CO2 and larger horizontal gradients. Not only did the mean north–south gradient increase due to fossil fuel emissions, but east–west contrasts in CO2 also strengthened because of changing patterns in fossil fuel emissions and terrestrial carbon exchange. In the RCP8.5 simulation, where CO2 increased to 1150 ppm by 2100, the CESM predicted increases in interannual variability in the Northern Hemisphere midlatitudes of up to 60% relative to present variability for time series filtered with a 2–10-yr bandpass. Such an increase in variability may impact detection of changing surface fluxes from atmospheric observations.

This study used an ocean general circulation model to simulate the marine iron cycle in an investigation of how simulated distributions of weak iron-binding ligands would be expected to control dissolved iron concentrations in the ocean, with a particular focus on deep ocean waters. The distribution of apparent oxygen utilization was used as a proxy for humic substances that have recently been hypothesized to account for the bulk of weak iron-binding ligands in seawater. Compared to simulations using a conventional approach with homogeneous ligand distributions, the simulations that incorporated spatially variable ligand concentrations exhibited substantial improvement in the simulation of global dissolved iron distributions as revealed by comparisons with available field data. The improved skill of the simulations resulted largely because the spatially variable ligand distributions led to a more reasonable basin-scale variation of the residence time of iron when present at high concentrations. The model results, in conjunction with evidence from recent field studies, suggest that humic substances play an important role in the iron cycle in the ocean.

Abstract The current coarse-resolution version of the Community Climate System Model is used to assess the impact of phytoplankton on El Niño–Southern Oscillation (ENSO). The experimental setup allows for the separation of the effects of climatological annual cycle of chlorophyll distribution from its interannually varying part. The main finding is that the chlorophyll production by phytoplankton is important beyond modifying the mean and seasonal cycle of shortwave absorption; interannual modifications to the absorption have an impact as well, and they dampen ENSO variability by 9%. The magnitude of damping is the same in the experiment with smaller-than-observed, and in the experiment with larger-than-observed, chlorophyll distribution. This result suggests that to accurately represent ENSO in GCMs, it is not sufficient to use a prescribed chlorophyll climatology. Instead, some form of an ecosystem model will be necessary to capture the effects of phytoplankton coupling and feedback.

With representation of the global carbon cycle becoming increasingly complex in climate models, it is important to develop ways to quantitatively evaluate model performance against in situ and remote sensing observations. Here we present a systematic framework, the Carbon-LAnd Model Intercomparison Project (C-LAMP), for assessing terrestrial biogeochemistry models coupled to climate models using observations that span a wide range of temporal and spatial scales. As an example of the value of such comparisons, we used this framework to evaluate two biogeochemistry models that are integrated within the Community Climate System Model (CCSM) -Carnegie-Ames-Stanford Approach′ (CASA′) and carbon-nitrogen (CN). Both models underestimated the magnitude of net carbon uptake during the growing season in temperate and boreal forest ecosystems, based on comparison with atmospheric CO 2 measurements and eddy covariance measurements of net ecosystem exchange. Comparison with MODerate Resolution Imaging Spectroradiometer (MODIS) measurements show that this low bias in model fluxes was caused, at least in part, by 1-3 month delays in the timing of maximum leaf area. In the tropics, the models overestimated carbon storage in woody biomass based on comparison with datasets from the Amazon. Reducing this model bias will probably weaken the sensitivity of terrestrial carbon fluxes to both atmospheric CO2 and climate. Global carbon sinks during the 1990s differed by a factor of two (2.4PgCyr-1 for CASA′ vs. 1.2PgCyr-1 for CN), with fluxes from both models compatible with the atmospheric budget given uncertainties in other terms. The models captured some of the timing of interannual global terrestrial carbon exchange during 1988-2004 based on comparison with atmospheric inversion results from TRANSCOM (r =0.66 for CASA′ and r =0.73 for CN). Adding (CASA′) or improving (CN) the representation of deforestation fires may further increase agreement with the atmospheric record. Information from C-LAMP has enhanced model performance within CCSM and serves as a benchmark for future development. We propose that an open source, community-wide platform for model-data intercomparison is needed to speed model development and to strengthen ties between modeling and measurement communities. Important next steps include the design and analysis of land use change simulations (in both uncoupled and coupled modes), and the entrainment of additional ecological and earth system observations. Model results from C-LAMP are publicly available on the Earth System Grid. © 2009 Blackwell Publishing Ltd.

We present results from transient sensitivity studies with the Biogeochemical Elemental Cycling (BEC) ocean model to increasing anthropogenic atmospheric inorganic nitrogen (N) and soluble iron (Fe) deposition over the industrial era. Elevated N deposition results from fossil fuel combustion and agriculture, and elevated soluble Fe deposition results from increased atmospheric processing in the presence of anthropogenic pollutants and soluble Fe from combustion sources. Simulations with increasing Fe and increasing Fe and N inputs raised simulated marine nitrogen fixation, with the majority of the increase in the subtropical North and South Pacific, and raised primary production and export in the high-nutrient low-chlorophyll (HNLC) regions. Increasing N inputs alone elevated small phytoplankton and diatom production, resulting in increased phosphorus (P) and Fe limitation for diazotrophs, hence reducing nitrogen fixation (∼6%). Globally, the simulated primary production, sinking particulate organic carbon (POC) export. and atmospheric CO2 uptake were highest under combined increase in Fe and N inputs compared to preindustrial control. Our results suggest that increasing combustion iron sources and aerosol Fe solubility along with atmospheric anthropogenic nitrogen deposition are perturbing marine biogeochemical cycling and could partially explain the observed trend toward increased P limitation at station ALOHA in the subtropical North Pacific. Excess inorganic nitrogen ([NO3 −] + [NH4 +] − 16[PO4 3−]) distributions may offer useful insights for understanding changing ocean circulation and biogeochemistry.

We quantify the mechanisms governing interannual variability in the global, upper-ocean inorganic carbon system using a hindcast simulation (1979-2004) of an ecosystem-biogeochemistry model forced with time-evolving atmospheric physics and dust deposition. We analyze the variability of three key, interrelated metrics-air-sea CO2 flux, surface-water carbon dioxide partial pressure pCO2, and upper-ocean dissolved inorganic carbon (DIC) inventory-presenting for each metric global spatial maps of the root mean square (rms) of anomalies from a model monthly climatology. The contribution of specific driving factors is diagnosed using Taylor expansions and linear regression analysis. The major regions of variability occur in the Southern Ocean, tropical Indo-Pacific, and Northern Hemisphere temperate and subpolar latitudes. Ocean circulation is the dominant factor driving variability over most of the ocean, modulating surface dissolved inorganic carbon that in turn alters surface-water pCO2 and air-sea CO2 flux variability (global integrated anomaly rms of 0.34 Pg C yr-1). Biological export and thermal solubility effects partially damp circulation-driven pCO2 variability in the tropics, while in the subtropics, thermal solubility contributes positively to surface-water pCO2 and air-sea CO2 flux variability. Gas transfer and net freshwater inputs induce variability in the air-sea CO2 flux in some specific regions. A component of air-sea CO2 flux variability (global integrated anomaly rms of 0.14 Pg C yr-1) arises from variations in biological export production induced by variations in atmospheric iron deposition downwind of dust source regions. Beginning in the mid-1990s, reduced global dust deposition generates increased air-sea CO2 outgassing in the Southern Ocean, consistent with trends derived from atmospheric CO2 inversions. © 2008 Elsevier Ltd.

We present a generalized framework for assessing the skill of global upper ocean ecosystem-biogeochemical models against in-situ field data and satellite observations. We illustrate the approach utilizing a multi-decade (1979-2004) hindcast experiment conducted with the Community Climate System Model (CCSM-3) ocean carbon model. The CCSM-3 ocean carbon model incorporates a multi-nutrient, multi-phytoplankton functional group ecosystem module coupled with a carbon, oxygen, nitrogen, phosphorus, silicon, and iron biogeochemistry module embedded in a global, three-dimensional ocean general circulation model. The model is forced with physical climate forcing from atmospheric reanalysis and satellite data products and time-varying atmospheric dust deposition. Data-based skill metrics are used to evaluate the simulated time-mean spatial patterns, seasonal cycle amplitude and phase, and subannual to interannual variability. Evaluation data include: sea surface temperature and mixed layer depth; satellite-derived surface ocean chlorophyll, primary productivity, phytoplankton growth rate and carbon biomass; large-scale climatologies of surface nutrients, pCO2, and air-sea CO2 and O2 flux; and time-series data from the Joint Global Ocean Flux Study (JGOFS). Where the data is sufficient, we construct quantitative skill metrics using: model-data residuals, time-space correlation, root mean square error, and Taylor diagrams. © 2008 Elsevier B.V. All rights reserved.

For several decades, a suite of satellite sensors has enabled us to study the global spatiotemporal distribution of phytoplankton through remote sensing of chlorophyll. However, the satellite record has extensive missing data, partially due to cloud cover; regions characterized by the highest phytoplankton abundance are also some of the cloudiest. To quantify potential sampling biases due to missing data, we developed a satellite simulator for ocean chlorophyll in the Community Earth System Model (CESM) that mimics what a satellite would detect if it were present in the model-generated world. Our Chlorophyll Observation Simulator Package (ChlOSP) generates synthetic chlorophyll observations at model runtime. ChlOSP accounts for missing data – due to low light, sea ice, and cloud cover – and it can implement swath sampling. Here, we introduce this new tool and present a preliminary study focusing on long timescales. Results from a 50-year pre-industrial control simulation of CESM–ChlOSP suggest that missing data impact the apparent mean state and variability of chlorophyll. The simulated observations exhibit a nearly -20 % difference in global mean chlorophyll compared with the standard model output, which is the same order of magnitude as the projected change in chlorophyll by the end of the century. Additionally, missing data impact the apparent seasonal cycle of chlorophyll in subpolar regions. We highlight four potential future applications of ChlOSP: (1) refined model tuning; (2) evaluating chlorophyll-based net primary productivity (NPP) algorithms; (3) revised time to emergence of anthropogenic chlorophyll trends; and (4) a test bed for the assessment of gap-filling approaches for missing satellite chlorophyll data. [ABSTRACT FROM AUTHOR]

A global ocean biogeochemical model is used to quantify the sensitivity of marine biogeochemistry and air–sea CO2 exchange to variations in dust deposition over decadal timescales. Estimates of dust deposition generated under four climate states provide a large range in total deposition with spatially realistic patterns; transient ocean model experiments are conducted by applying a step-function change in deposition from a current climate control. Relative to current conditions, higher dust deposition increases diatom and export production, nitrogen fixation and oceanic net CO2 uptake from the atmosphere, while reduced dust deposition has the opposite effects. Over timescales less than a decade, dust modulation of marine productivity and export is dominated by direct effects in high-nutrient, low-chlorophyll regions, where iron is the primary limiting nutrient. On longer timescales, an indirect nitrogen fixation pathway has increased importance, significantly amplifying the ocean biogeochemical response. Because dust iron input decouples carbon cycling from subsurface macronutrient supply, the ratio of the change in net ocean CO2 uptake to change in export flux is large, 0.45–0.6. Decreasing dust deposition and reduced oceanic CO2 uptake over the next century could provide a positive feedback to global warming, distinct from feedbacks associated with changes in stratification and circulation.

A global three-dimensional marine ecosystem model with several key phytoplankton functional groups, multiple limiting nutrients, explicit iron cycling, and a mineral ballast/organic matter parameterization is run within a global ocean circulation model. The coupled biogeochemistry/ecosystem/circulation (BEC) model reproduces known basin-scale patterns of primary and export production, biogenic silica production, calcification, chlorophyll, macronutrient and dissolved iron concentrations. The model captures observed high nitrate, low chlorophyll (HNLC) conditions in the Southern Ocean, subarctic and equatorial Pacific. Spatial distributions of nitrogen fixation are in general agreement with field data, with total N-fixation of 55 Tg N. Diazotrophs directly account for a small fraction of primary production (0.5%) but indirectly support 10% of primary production and 8% of sinking particulate organic carbon (POC) export. Diatoms disproportionately contribute to export of POC out of surface waters, but CaCO3 from the coccolithophores is the key driver of POC flux to the deep ocean in the model. An iron source from shallow ocean sediments is found critical in preventing iron limitation in shelf regions, most notably in the Arctic Ocean, but has a relatively localized impact. In contrast, global-scale primary production, export production, and nitrogen fixation are all sensitive to variations in atmospheric mineral dust inputs. The residence time for dissolved iron in the upper ocean is estimated to be a few years to a decade. Most of the iron utilized by phytoplankton is from subsurface sources supplied by mixing, entrainment, and ocean circulation. However, owing to the short residence time of iron in the upper ocean, this subsurface iron pool is critically dependent on continual replenishment from atmospheric dust deposition and, to a lesser extent, lateral transport from shelf regions.

One of the central objectives of the Joint Global Ocean Flux Study (JGOFS) is to use data from the extensive field programs to evaluate and improve numerical ocean carbon-cycle models. Substantial improvements are required if we are to achieve a better understanding of present-day biogeochemical properties and processes in the ocean and to predict potential future responses to perturbations resulting from human activities. We have made significant progress in this regard and expect even greater strides over the next decade as the synthesis of JGOFS data sets is completed and disseminated to the broader scientific community.

For several decades, a suite of satellite sensors has enabled us to study the global spatiotemporal distribution of phytoplankton through remote sensing of chlorophyll. However, the satellite record has extensive missing data, partially due to cloud cover; regions characterized by the highest phytoplankton abundance are also some of the cloudiest. To quantify potential sampling biases due to missing data, we developed a satellite simulator for ocean chlorophyll in the Community Earth System Model (CESM) that mimics what a satellite would detect if it were present in the model-generated world. Our Chlorophyll Observation Simulator Package (ChlOSP) generates synthetic chlorophyll observations at model runtime. ChlOSP accounts for missing data - due to low light, sea ice, and cloud cover - and it can implement swath sampling. Results from a 50-year pre-industrial control simulation of CESM-ChlOSP suggest that missing data impacts the apparent mean state and variability of chlorophyll. The simulated observations exhibit a nearly -20 % difference in global mean chlorophyll compared with the standard model output, which is the same order of magnitude as the projected change in chlorophyll by the end of the century. Additionally, missing data impacts the apparent seasonal cycle of chlorophyll in subpolar regions. We highlight four potential future applications of ChlOSP: (1) refined model tuning, (2) evaluating chlorophyll-based NPP algorithms, (3) revised time to emergence of anthropogenic chlorophyll trends, and (4) a testbed for the assessment of gap-filling approaches for missing satellite chlorophyll data. [ABSTRACT FROM AUTHOR]

The ocean has absorbed 41 per cent of all anthropogenic carbon emitted as a result of fossil fuel burning and cement manufacture (1,2). The magnitude and the large-scale distribution of the ocean carbon sink is well quantified for recent decades (3,4). In contrast, temporal changes in the oceanic carbon sink remain poorly understood (5-7). It has proved difficult to distinguish between air-to-sea carbon flux trends that are due to anthropogenic climate change and those due to internal climate variability (5,6,8-13). Here we use a modelling approach that allows for this separation (14), revealing how the ocean carbon sink may be expected to change throughout this century in different oceanic regions. Our findings suggest that, owing to large internal climate variability, it is unlikely that changes in the rate of anthropogenic carbon uptake can be directly observed in most oceanic regions at present, but that this may become possible between 2020 and 2050 in some regions., Recent observationally based syntheses have quantified mean ocean carbon uptake and its spatial distribution (1,3,4,15) (Extended Data Fig. 1). In addition, interior ocean observations analysed under the assumption of constant [...]