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Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land‐atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia ‐‐ along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model (LSM) performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry‐season increases in evapotranspiration was associated with model over‐estimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20‐30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10 to 15% of incoming solar radiation, compared to 0.15‐0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry‐season evapotranspiration and associated cooling). These partially compensating model‐observation discrepancies (e.g. higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation (Rn), the key driver of water and energy fluxes (LE ~ 0.6Rn and H ~ 0.15Rn). Though these biases varied among sites and models. A better representation of energy‐related parameters associated with dynamic phenology (e.g. leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation‐atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here, we present an approach for integrating multivariate trait data into a predictive model of trait evolution. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO 2 . We found that the direction of historical bias (existing trait correlations) influenced both the rate of adaptation and the evolved phenotypes (trait combinations). Critically, we use fitness landscapes derived directly from empirical trait values to capture known evolutionary phenomena. This work demonstrates that ecological models need to represent both changes in traits and changes in the correlation between traits in order to accurately capture phytoplankton evolution and predict future shifts in elemental cycling.

The Ecosystem Demography model version 2.2 (ED-2.2) is a terrestrial biosphere model that simulates the biophysical, ecological, and biogeochemical dynamics of vertically and horizontally heterogeneous terrestrial ecosystems. In a companion paper (Longo et al., 2019a), we described how the model solves the energy, water, and carbon cycles, and verified the high degree of conservation of these properties in long-term simulations that include long-term (multi-decadal) vegetation dynamics. Here, we present a detailed assessment of the model's ability to represent multiple processes associated with the biophysical and biogeochemical cycles in Amazon forests. We use multiple measurements from eddy covariance towers, forest inventory plots, and regional remote-sensing products to assess the model's ability to represent biophysical, physiological, and ecological processes at multiple timescales, ranging from subdaily to century long. The ED-2.2 model accurately describes the vertical distribution of light, water fluxes, and the storage of water, energy, and carbon in the canopy air space, the regional distribution of biomass in tropical South America, and the variability of biomass as a function of environmental drivers. In addition, ED-2.2 qualitatively captures several emergent properties of the ecosystem found in observations, specifically observed relationships between aboveground biomass, mortality rates, and wood density; however, the slopes of these relationships were not accurately captured. We also identified several limitations, including the model's tendency to overestimate the magnitude and seasonality of heterotrophic respiration and to overestimate growth rates in a nutrient-poor tropical site. The evaluation presented here highlights the potential of incorporating structural and functional heterogeneity within biomes in Earth system models (ESMs) and to realistically represent their impacts on energy, water, and carbon cycles. We also identify several priorities for further model development.

Significance Robust predictions of future changes in global biogeochemical cycling require an understanding of how microorganisms adapt to stressful and changing environments. In the ocean, rates of adaptation will be a function of both evolutionary timescales and physical dynamics. However, little is known about this interaction. We examined evolutionary dynamics of marine microbes by combining a model of microbial adaptation with varying selection pressures with a high-resolution ocean circulation model. A trade-off emerged between two evolutionary strategies: (i) ability to adapt plastically to short-term environmental fluctuations with delayed genetic adaptation and (ii) more rapid genetic adaptation with limited response to short-term environmental fluctuations. This trade-off determines evolutionary timescales and provides a foundation for understanding distributions of microbial traits and biogeochemistry., Marine microbes form the base of ocean food webs and drive ocean biogeochemical cycling. Yet little is known about the ability of microbial populations to adapt as they are advected through changing conditions. Here, we investigated the interplay between physical and biological timescales using a model of adaptation and an eddy-resolving ocean circulation climate model. Two criteria were identified that relate the timing and nature of adaptation to the ratio of physical to biological timescales. Genetic adaptation was impeded in highly variable regimes by nongenetic modifications but was promoted in more stable environments. An evolutionary trade-off emerged where greater short-term nongenetic transgenerational effects (low-γ strategy) enabled rapid responses to environmental fluctuations but delayed genetic adaptation, while fewer short-term transgenerational effects (high-γ strategy) allowed faster genetic adaptation but inhibited short-term responses. Our results demonstrate that the selective pressures for organisms within a single water mass vary based on differences in generation timescales resulting in different evolutionary strategies being favored. Organisms that experience more variable environments should favor a low-γ strategy. Furthermore, faster cell division rates should be a key factor in genetic adaptation in a changing ocean. Understanding and quantifying the relationship between evolutionary and physical timescales is critical for robust predictions of future microbial dynamics.

The cyanobacterium Trichodesmium fixes as much as half of the nitrogen (N2) that supports tropical open-ocean biomes, but its growth is frequently limited by iron (Fe) availability1,2. How future ocean warming may interact with this globally widespread Fe limitation of Trichodesmium N2 fixation is unclear3. Here, we show that the optimum growth temperature of Fe-limited Trichodesmium is ~5 °C higher than for Fe-replete cells, which results in large increases in growth and N2 fixation under the projected warmer Fe-deplete sea surface conditions. Concurrently, the cellular Fe content decreases as temperature rises. Together, these two trends result in thermally driven increases of ~470% in Fe-limited cellular iron use efficiencies (IUEs), defined as the molar quantity of N2 fixed by Trichodesmium per unit time per mole of cellular Fe (mol N2 fixed h–1 mol Fe–1), which enables Trichodesmium to much more efficiently leverage the scarce available Fe supplies to support N2 fixation. Modelling these results in the context of the IPCC representative concentration pathway (RCP) 8.5 global warming scenario4 predicts that IUEs of N2 fixers could increase by ~76% by 2100, and largely alleviate the prevailing Fe limitation across broad expanses of the tropical Pacific and Indian Oceans. Thermally enhanced cyanobacterial IUEs could increase future global marine N2 fixation by ~22% over the next century, and thus profoundly alter the biology and biogeochemistry of open-ocean ecosystems. The growth of nitrogen-fixing marine cyanobacteria Trichodesmium is limited by iron availability under current conditions. However warmer temperatures reduce the iron requirement, allowing greater growth rates and increased nitrogen fixation.

Ocean boundary current systems are key components of the climate system, are home to highly productive ecosystems, and have numerous societal impacts. Establishment of a global network of boundary current observing systems is a critical part of ongoing development of the Global Ocean Observing System. The characteristics of boundary current systems are reviewed, focusing on scientific and societal motivations for sustained observing. Techniques currently used to observe boundary current systems are reviewed, followed by a census of the current state of boundary current observing systems globally. The next steps in the development of boundary current observing systems are considered, leading to several specific recommendations. Fil: Todd, Robert E.. Woods Hole Oceanographic Institution; Estados Unidos Fil: Chavez, Francisco. Monterey Bay Aquarium Research Institute; Estados Unidos Fil: Clayton, Sophie. Old Dominion University; Estados Unidos Fil: Cravatte, Sophie E.. Centre National de la Recherche Scientifique. Institut de Recherche pour le Développement; Francia. Universite de Toulouse; Francia Fil: Goes, Marlos P.. University of Miami; Estados Unidos Fil: Graco, Michelle I.. Instituto del Mar del Peru; Perú Fil: Lin, Xiaopei. Ocean University of China; China Fil: Sprintall, Janet. University of California; Estados Unidos Fil: Zilberman, Nathalie V.. University of California; Estados Unidos Fil: Archer, Matthew. California Institute of Technology; Estados Unidos Fil: Arístegui, Javier. Universidad de Las Palmas de Gran Canaria; España Fil: Balmaseda, Magdalena A.. European Centre for Medium-Range Weather Forecasts; Reino Unido Fil: Bane, John M.. University of North Carolina; Estados Unidos Fil: Baringer, Molly O.. Atlantic Oceanographic and Meteorological Laboratory ; Estados Unidos Fil: Barth, John A.. State University of Oregon; Estados Unidos Fil: Beal, Lisa M.. University of Miami; Estados Unidos Fil: Brandt, Peter. Geomar-Helmholtz Centre for Ocean Research Kiel; Alemania Fil: Calil, Paulo H.. Universidade Federal do Rio Grande; Brasil Fil: Campos, Edmo. Universidade de Sao Paulo; Brasil Fil: Centurioni, Luca R.. University of California; Estados Unidos Fil: Chidichimo, María Paz. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Armada Argentina. Servicio de Hidrografía Naval; Argentina Fil: Cirano, Mauro. Universidade Federal do Rio de Janeiro; Brasil Fil: Cronin, Meghan F.. National Oceanic and Atmospheric Administration. Pacific Marine Environmental Laboratory; Estados Unidos Fil: Curchitser, Enrique N.. Rutgers University; Estados Unidos Fil: Davis, Russ E.. University of California; Estados Unidos Fil: Dengler, Marcus. Geomar-Helmholtz Centre for Ocean Research Kiel; Alemania Fil: DeYoung, Brad. Memorial University of Newfoundland; Canadá Fil: Dong, Shenfu. University of Miami; Estados Unidos Fil: Escribano, Ruben. Universidad de Concepción; Chile Fil: Fassbender, Andrea J.. Monterey Bay Aquarium Research Institute; Estados Unidos

Remineralization of organic matter by heterotrophic organisms regulates the biological sequestration of carbon, thereby mediating atmospheric CO2. While surface nutrient supply impacts the elemental ratios of primary production, stoichiometric control by remineralization remains unclear. Here we develop a mechanistic description of remineralization and its stoichiometry in a marine microbial ecosystem model. The model simulates the observed elemental plasticity of phytoplankton and the relatively constant, lower C:N of heterotrophic biomass. In addition, the model captures the observed decreases in DOC:DON and the C:N remineralization ratio with depth for more labile substrates, which are driven by a switch in the dominant source of labile DOM from phytoplankton to heterotrophic biomass. Only a model version with targeted remineralization of N-rich components is able to simulate the observed profiles of preferential remineralization of DON relative to DOC and the elevated C:N of bulk DOM. The model suggests that more labile substrates are associated with C-limited heterotrophic growth and not with preferential remineralization, while more recalcitrant substrates are associated with growth limited by processing rates and with preferential remineralization. The resulting patterns of variable remineralization stoichiometry mediate the extent to which a proportional increase in carbon production resulting from changes in phytoplankton stoichiometry can increase the efficiency of the biological pump. Results emphasize the importance of understanding the physiology of both phytoplankton and heterotrophs for anticipating changes in biologically driven ocean carbon storage.

Sustained ocean time series are critical for characterizing marine ecosystem shifts in a time of accelerating, and at times unpredictable, changes. They represent the only means to distinguish between natural and anthropogenic forcings, and are the best tools to explore causal links and implications for human communities that depend on ocean resources. Since the inception of sustained ocean observations, ocean time series have withstood many challenges, most prominently availability of uninterrupted funding and retention of trained personnel. This OceanObs’19 review article provides an overarching vision for sustained ocean time series observations for the next decade, focusing on the growing challenges of maintaining sustained ocean time series, including ship-based and autonomous coastal and open-ocean platforms, as well as remote sensing. In addition to increased diversification of funding sources to include the private sector, NGOs, and other groups, more effective engagement of stakeholders and other end-users will be critical to ensure the sustainability of ocean time series programs. Building a cohesive international time series network will require dedicated capacity to coordinate across observing programs and leverage existing infrastructure and platforms of opportunity. This review article outlines near-term observing priorities and technology needs; explores potential mechanisms to broaden ocean time series data applications and end-user communities; and describes current tools and future requirements for managing increasingly complex multi-platform data streams and developing synthesis products that support science and society. The actionable recommendations outlined herein ultimately form the basis for a robust, sustainable, fit-for-purpose time series network that will foster a predictive understanding of changing ocean systems for the benefit of society.

Marine microbes form the base of ocean food webs and drive ocean biogeochemical cycling. Yet little is known about how microbial populations will evolve due to global change-driven shifts in ocean dynamics. Understanding adaptive timescales is critical where long-term trends (e.g. warming) are coupled to shorter-term advection dynamics that move organisms rapidly between ecoregions. Here we investigated the interplay between physical and biological timescales using a model of adaptation and an eddy-resolving ocean circulation climate model. Two criteria (α and β) were identified that relate physical and biological timescales and determine the timing and nature of adaptation. Genetic adaptation was impeded in highly variable regimes (α1). An evolutionary trade-off emerged where greater short-term transgenerational effects (low-β-strategy) enabled rapid responses to environmental fluctuations but delayed genetic adaptation, while fewer short-term transgenerational effects (high-β-strategy) allowed faster genetic adaptation but inhibited short-term responses. Our results suggest that organisms with faster growth rates are better positioned to adapt to rapidly changing ocean conditions and that more variable environments will favor a bet-hedging, low-β-strategy. Understanding the relationship between evolutionary and physical timescales is critical for robust predictions of future microbial dynamics.

In the subtropical ocean, physical and biogeochemical patchiness exists across a range of spatial and temporal scales. As ocean surface temperatures rise, climate models suggest that the strength of the basin-scale biological carbon pump may diminish due to increased stratification and depleted surface nutrients. However, global model predictions often cannot account for climate-physical-ecosystem interactions occurring at finer spatial scales (e.g., less than a few tens of kilometers). In order to study such fine-scale interactions, we developed a computationally tractable, idealized modeling framework that allows for a systematic assessment of the impact of fine-scale frontal disturbances (i.e., short-lived upwelling and downwelling events) on biogeochemical and ecosystem dynamics. The model successfully captured both the mean dynamics and the range of ecosystem variability observed at the Hawaii Ocean Time-series (HOT) site in the oligotrophic North Pacific Subtropical Gyre. Fine-scale frontal disturbances were shown to impact both phytoplankton assemblages and carbon cycling. Specifically, disturbances of shorter duration but higher intensity favored the growth of large phytoplankton and contributed to elevated carbon export efficiency. However, for disturbances of longer duration and higher intensity, a decoupling between physical changes and biological responses led to a reduced utilization efficiency of upwelled nutrients and thus a reduction in new production. Our results emphasize that future changes in both large and fine-scale physical dynamics may play an important role in shaping marine ecosystems, and could be accounted for in the next-generation global climate models using this type of parameterization.

Subtropical gyres contribute significantly to global ocean productivity. As the climate warms, the strength of these gyres as a biological carbon pump is predicted to diminish due to increased stratification and depleted surface nutrients. We present results suggesting that the impact of submesoscale physics on phytoplankton in the oligotrophic ocean is substantial and may either compensate or exacerbate future changes in carbon cycling. A new statistical tool was developed to quantify surface patchiness from sea surface temperatures. Chlorophyll concentrations in the North Pacific Subtropical Gyre were shown to be enhanced by submesoscale frontal dynamics with an average increase of 38% (maximum of 83%) during late winter. The magnitude of this enhancement is comparable to the observed decline in chlorophyll due to a warming of ~1.1°C. These results highlight the need for an improved understanding of fine-scale physical variability in order to predict the response of marine ecosystems to projected climate changes.

© 2018 The Authors. New Phytologist © 2018 New Phytologist Trust The impact of increases in drought frequency on the Amazon forest's composition, structure and functioning remain uncertain. We used a process- and individual-based ecosystem model (ED2) to quantify the forest's vulnerability to increased drought recurrence. We generated meteorologically realistic, drier-than-observed rainfall scenarios for two Amazon forest sites, Paracou (wetter) and Tapajós (drier), to evaluate the impacts of more frequent droughts on forest biomass, structure and composition. The wet site was insensitive to the tested scenarios, whereas at the dry site biomass declined when average rainfall reduction exceeded 15%, due to high mortality of large-sized evergreen trees. Biomass losses persisted when year-long drought recurrence was shorter than 2–7 yr, depending upon soil texture and leaf phenology. From the site-level scenario results, we developed regionally applicable metrics to quantify the Amazon forest's climatological proximity to rainfall regimes likely to cause biomass loss > 20% in 50 yr according to ED2 predictions. Nearly 25% (1.8 million km2) of the Amazon forests could experience frequent droughts and biomass loss if mean annual rainfall or interannual variability changed by 2σ. At least 10% of the high-emission climate projections (CMIP5/RCP8.5 models) predict critically dry regimes over 25% of the Amazon forest area by 2100.

Oceanic time series have been instrumental in providing an understanding of biological, physical, and chemical dynamics in the oceans and how these processes change over time. However, the extrapolation of these results to larger oceanographic regions requires an understanding and characterization of local versus regional drivers of variability. Here we use high-frequency spatial and temporal glider data to quantify variability at the coastal San Pedro Ocean Time-series (SPOT) site in the San Pedro Channel (SPC) and provide insight into the underlying oceanographic dynamics for the site. The dataset could be described by a combination of four water column profile types that typified active upwelling, a surface bloom, warm-stratified low-nutrient conditions, and a subsurface chlorophyll maximum. On weekly timescales, the SPOT station was on average representative of 64 % of profiles taken within the SPC. In general, shifts in water column profile characteristics at SPOT were also observed across the entire channel. On average, waters across the SPC were most similar to offshore profiles, suggesting that SPOT time series data would be more impacted by regional changes in circulation than local coastal events. These results indicate that high-resolution in situ glider deployments can be used to quantify major modes of variability and provide context for interpreting time series data, allowing for broader application of these datasets and greater integration into modeling efforts.

A fundamental question connecting terrestrial ecology and global climate change is the sensitivity of key terrestrial biomes to climatic variability and change. The Amazon region is such a key biome: it contains unparalleled biological diversity, a globally significant store of organic carbon, and it is a potent engine driving global cycles of water and energy. The importance of understanding how land surface dynamics of the Amazon region respond to climatic variability and change is widely appreciated, but despite significant recent advances, large gaps in our understanding remain. Understanding of energy and carbon exchange between terrestrial ecosystems and the atmosphere can be improved through direct observations and experiments, as well as through modeling activities. Land surface/ecosystem models have become important tools for extrapolating local observations and understanding to much larger terrestrial regions. They are also valuable tools to test hypothesis on ecosystem functioning. Funded by NASA under the auspices of the LBA (the Large-Scale Biosphere-Atmosphere Experiment in Amazonia), the LBA Data Model Intercomparison Project (LBA-DMIP) uses a comprehensive data set from an observational network of flux towers across the Amazon, and an ecosystem modeling community engaged in ongoing studies using a suite of different land surface and terrestrial ecosystem models to understand Amazon forest function. Here an overview of this project is presented accompanied by a description of the measurement sites, data, models and protocol. © 2013 Elsevier B.V.

To predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuana CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, ‘soil water stress’ and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re) at equatorial locations. In contrast, a southern Amazon forest (Jaru RJA) exhibited dry-season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.

Amazon forests, which store ∼ 50% of tropical forest carbon and play a vital role in global water, energy, and carbon cycling, are predicted to experience both longer and more intense dry seasons by the end of the 21st century. However, the climate sensitivity of this ecosystem remains uncertain: several studies have predicted large-scale die-back of the Amazon, whereas several more recent studies predict that the biome will remain largely intact. Combining remote-sensing and ground-based observations with a size- and age-structured terrestrial ecosystem model, we explore the sensitivity and ecological resilience of these forests to changes in climate. We demonstrate that water stress operating at the scale of individual plants, combined with spatial variation in soil texture, explains observed patterns of variation in ecosystem biomass, composition, and dynamics across the region, and strongly influences the ecosystem's resilience to changes in dry season length. Specifically, our analysis suggests that in contrast to existing predictions of either stability or catastrophic biomass loss, the Amazon forest's response to a drying regional climate is likely to be an immediate, graded, heterogeneous transition from high-biomass moist forests to transitional dry forests and woody savannah-like states. Fire, logging, and other anthropogenic disturbances may, however, exacerbate these climate change-induced ecosystem transitions.

This study analyzes the inter-annual variability (IAV) of simulations of 21 different land surface model formulations, driven by meteorological conditions measured at 8 flux towers, located in rain forest, forest-savanna ecotone and pasture sites in Amazonia, and one in savanna site in Southeastern Brazil. Annual totals of net ecosystem exchange (NEE) of carbon and evapotranspiration (ET), measured and simulated by each model for each site-year, were compared in terms of year-to-year variability and possible relation to climate drivers. Results have shown that most of models simulations for annual totals of NEE and ET, and IAV of these fluxes, are frequently different from measurements. The average of the model simulations of annual fluxes tend to respond to climatic drivers similarly to the observations, but with noticeable discrepancies. Annual measurements of NEE are negatively correlated to annual rainfall in the forest sites group. Although the ensemble of all models yields a similar result, only three model formulations reproduce a significant negative correlation of simulated NEE with rainfall. For the IAV of ET, tower measurements are controlled by annual variations of radiation and this feature is captured by the ensemble of the models, both at individual sites and when all forest sites are grouped. However, simulated ET values are also significantly correlated to the amount of precipitation in many models and in the model ensemble, while there is no significant correlation in the observations. In general, the surface models are able to reproduce the responses of fluxes to climatic drivers, but improvements are still needed to better capture their inter-annual variability. © 2013 Elsevier B.V..

Considerable uncertainty surrounds the fate of Amazon rainforests in response to climate change. Here, carbon (C) flux predictions of five terrestrial biosphere models (Community Land Model version 3.5 (CLM3.5), Ecosystem Demography model version 2.1 (ED2), Integrated BIosphere Simulator version 2.6.4 (IBIS), Joint UK Land Environment Simulator version 2.1 (JULES) and Simple Biosphere model version 3 (SiB3)) and a hydrodynamic terrestrial ecosystem model (the Soil-Plant-Atmosphere (SPA) model) were evaluated against measurements from two large-scale Amazon drought experiments. Model predictions agreed with the observed C fluxes in the control plots of both experiments, but poorly replicated the responses to the drought treatments. Most notably, with the exception of ED2, the models predicted negligible reductions in aboveground biomass in response to the drought treatments, which was in contrast to an observed c. 20% reduction at both sites. For ED2, the timing of the decline in aboveground biomass was accurate, but the magnitude was too high for one site and too low for the other. Three key findings indicate critical areas for future research and model development. First, the models predicted declines in autotrophic respiration under prolonged drought in contrast to measured increases at one of the sites. Secondly, models lacking a phenological response to drought introduced bias in the sensitivity of canopy productivity and respiration to drought. Thirdly, the phenomenological water-stress functions used by the terrestrial biosphere models to represent the effects of soil moisture on stomatal conductance yielded unrealistic diurnal and seasonal responses to drought.

Environmental context Microscopic marine organisms have the potential to influence the global climate through the production of a trace gas, dimethylsulfide, which contributes to cloud formation. Using 3 years of observations, we investigated the environmental drivers behind the production and degradation of dimethylsulfide and its precursor dimethylsulfoniopropionate. Our results highlight the important role of the microbial community in rapidly cycling these compounds and provide an important dataset for future modelling studies. Abstract Oceanic biogeochemical cycling of dimethylsulfide (DMS), and its precursor dimethylsulfoniopropionate (DMSP), has gained considerable attention over the past three decades because of the potential role of DMS in climate mediation. Here we report 3 years of monthly vertical profiles of organic sulfur cycle concentrations (DMS, particulate DMSP (DMSPp) and dissolved DMSP (DMSPd)) and rates (DMSPd consumption, biological DMS consumption and DMS photolysis) from the Bermuda Atlantic Time-series Study (BATS) site taken between 2005 and 2008. Concentrations confirm the summer paradox with mixed layer DMS peaking ~90 days after peak DMSPp and ~50 days after peak DMSPp:Chl. A small decline in mixed layer DMS was observed relative to those measured during a previous study at BATS (1992–1994), potentially driven by long-term climate shifts at the site. On average, DMS cycling occurred on longer timescales than DMSPd (0.43±0.35 v. 1.39±0.76 day–1) with DMSPd consumption rates remaining elevated throughout the year despite significant seasonal variability in the bacterial DMSP degrader community. DMSPp was estimated to account for 4–5% of mixed layer primary production and turned over at a significantly slower rate (~0.2 day–1). Photolysis drove DMS loss in the mixed layer during the summer, whereas biological consumption of DMS was the dominant loss process in the winter and at depth. These findings offer new insight into the underlying mechanisms driving DMS(P) cycling in the oligotrophic ocean, provide an extended dataset for future model evaluation and hypothesis testing and highlight the need for a reexamination of past modelling results and conclusions drawn from data collected with old methodologies.