Singular Vector Based Targeted Observations of Atmospheric Chemical Compounds

Measurements of the earth's environment provide only sparse snapshots of the state of the system due to their insufficient temporal and spatial density. In face of these limitations, the measurement configurations need to be optimized to get a best possible state estimate. One possibility to optimize the state estimate is provided by targeted observations of sensitive system states, where measurements are of great value for forecast improvements. In the recent years, numerical weather prediction adapted singular vector analysis with respect to initial values as a novel method to identify sensitive locations. In the present work, this technique was transferred from meteorological to chemical forecast. Besides initial values, emissions were introduced as controlling variables. Since time-variant amounts of emissions continuously act on the chemical evolution, targeting observations of emissions is a challenging task. Alternatively, uncertainties in the amplitude of the diurnal profile of emissions are analyzed, yielding emission factors as target variables. Special operators were designed to address specific questions of atmospheric chemistry, like the VOC- versus NOx-sensitivity of the ozone formation. The concept of adaptive observations was studied on two levels of complexity: At first, targeted singular vectors were implemented in a chemistry box model which only treats chemical reaction kinetics. Due to the absence of spatial dimensions, the chemistry box model only provides ranking lists of measurement priorities for different compounds. In the second step, singular vector analysis was implemented in a chemical transport model to additionally determine optimal placements of measurements. Both models have been tested and evaluated by conducting a comprehensive set of case studies. Particular questions of specific interest for the chemical system were examined, where the newly designed operators were applied. Results show large differences in sensitivities of different compounds. Consequently, an optimal measurement configuration benefits from omitting measurements of compounds of low sensitivity. It is demonstrated how targeted observations of chemical compounds depend on the considered simulation interval, meteorological conditions, and the underlying chemical composition. Accomplished studies clearly identify strong differences between meteorological and chemical target areas. These differences reveal the importance of the chemical composition and emphasize the significance of chemical singular vectors for effective campaign planing

The emergence of the Gulf Stream and interior western boundary as key regions to constrain the future North Atlantic carbon uptake

In recent years, the growing number of available climate models and future scenarios has led to emergent constraints becoming a popular tool to constrain uncertain future projections. However, when emergent constraints are applied over large areas, it is unclear (i) if the well-performing models simulate the correct dynamics within the considered area, (ii) which key dynamical features the emerging constraint is stemming from, and (iii) if the observational uncertainty is low enough to allow for a considerable reduction in the projection uncertainties. We therefore propose to regionally optimize emergent relationships with the twofold goal to (a) identify key model dynamics associated with the emergent constraint and model inconsistencies around them and (b) provide key areas where a narrow observational uncertainty is crucial for constraining future projections. Here, we consider two previously established emergent constraints of the future carbon uptake in the North Atlantic (Goris et al., 2018). For the regional optimization, we use a genetic algorithm and pre-define a suite of shapes and size ranges for the desired regions. Independent of pre-defined shape and size range, the genetic algorithm persistently identifies the Gulf Stream region centred around 30∘ N as optimal as well as the region associated with broad interior southward volume transport centred around 26∘ N. Close to and within our optimal regions, observational data of volume transport are available from the RAPID array with relative low observational uncertainty. Yet, our regionally optimized emergent constraints show that additional measures of specific biogeochemical variables along the array will fundamentally improve our estimates of the future carbon uptake in the North Atlantic. Moreover, our regionally optimized emergent constraints demonstrate that models that perform well for the upper-ocean volume transport and related key biogeochemical properties do not necessarily reproduce the interior-ocean volume transport well, leading to inconsistent gradients of key biogeochemical properties. This hampers the applicability of emergent constraints over large areas and highlights the need to additionally evaluate spatial model features.</p

Stratification constrains future heat and carbon uptake in the Southern Ocean between 30°S and 55°S

The Southern Ocean between 30°S and 55°S is a major sink of excess heat and anthropogenic carbon, but model projections of these sinks remain highly uncertain. Reducing such uncertainties is required to effectively guide the development of climate mitigation policies for meeting the ambitious climate targets of the Paris Agreement. Here, we show that the large spread in the projections of future excess heat uptake efficiency and cumulative anthropogenic carbon uptake in this region are strongly linked to the models’ contemporary stratification. This relationship is robust across two generations of Earth system models and is used to reduce the uncertainty of future estimates of the cumulative anthropogenic carbon uptake by up to 53% and the excess heat uptake efficiency by 28%. Our results highlight that, for this region, an improved representation of stratification in Earth system models is key to constrain future carbon budgets and climate change projections.publishedVersio

The ocean carbon sink – impacts, vulnerabilities and challenges

Carbon dioxide (CO2) is, next to water vapour, considered to be the most important natural greenhouse gas on Earth. Rapidly rising atmospheric CO2 concentrations caused by human actions such as fossil fuel burning, land-use change or cement production over the past 250 years have given cause for concern that changes in Earth’s climate system may progress at a much faster pace and larger extent than during the past 20 000 years. Investigating global carbon cycle pathways and finding suitable adaptation and mitigation strategies has, therefore, become of major concern in many research fields. The oceans have a key role in regulating atmospheric CO2 concentrations and currently take up about 25% of annual anthropogenic carbon emissions to the atmosphere. Questions that yet need to be answered are what the carbon uptake kinetics of the oceans will be in the future and how the increase in oceanic carbon inventory will affect its ecosystems and their services. This requires comprehensive investigations, including high-quality ocean carbon measurements on different spatial and temporal scales, the management of data in sophisticated databases, the application of Earth system models to provide future projections for given emission scenarios as well as a global synthesis and outreach to policy makers. In this paper, the current understanding of the ocean as an important carbon sink is reviewed with respect to these topics. Emphasis is placed on the complex interplay of different physical, chemical and biological processes that yield both positive and negative air–sea flux values for natural and anthropogenic CO2 as well as on increased CO2 (uptake) as the regulating force of the radiative warming of the atmosphere and the gradual acidification of the oceans. Major future ocean carbon challenges in the fields of ocean observations, modelling and process research as well as the relevance of other biogeochemical cycles and greenhouse gases are discussed

Assessment of Global Ocean Biogeochemistry Models for Ocean Carbon Sink Estimates in RECCAP2 and Recommendations for Future Studies

The ocean is a major carbon sink and takes up 25%–30% of the anthropogenically emitted CO2. A state-of-the-art method to quantify this sink are global ocean biogeochemistry models (GOBMs), but their simulated CO2 uptake differs between models and is systematically lower than estimates based on statistical methods using surface ocean pCO2 and interior ocean measurements. Here, we provide an in-depth evaluation of ocean carbon sink estimates from 1980 to 2018 from a GOBM ensemble. As sources of inter-model differences and ensemble-mean biases our study identifies (a) the model setup, such as the length of the spin-up, the starting date of the simulation, and carbon fluxes from rivers and into sediments, (b) the simulated ocean circulation, such as Atlantic Meridional Overturning Circulation and Southern Ocean mode and intermediate water formation, and (c) the simulated oceanic buffer capacity. Our analysis suggests that a late starting date and biases in the ocean circulation cause a too low anthropogenic CO2 uptake across the GOBM ensemble. Surface ocean biogeochemistry biases might also cause simulated anthropogenic fluxes to be too low, but the current setup prevents a robust assessment. For simulations of the ocean carbon sink, we recommend in the short-term to (a) start simulations at a common date before the industrialization and the associated atmospheric CO2 increase, (b) conduct a sufficiently long spin-up such that the GOBMs reach steady-state, and (c) provide key metrics for circulation, biogeochemistry, and the land-ocean interface. In the long-term, we recommend improving the representation of these metrics in the GOBMs

Evaluation of NorESM-OC (versions 1 and 1.2), the ocean carbon-cycle stand-alone configuration of the Norwegian Earth System Model (NorESM1)

Idealised and hindcast simulations performed with the stand-alone ocean carbon-cycle configuration of the Norwegian Earth System Model (NorESM-OC) are described and evaluated. We present simulation results of two different model versions at different grid resolutions and using two different atmospheric forcing data sets. Model version NorESM-OC1 corresponds to the version that is included in the fully coupled model NorESM-ME1, which participated in CMIP5. The main update between NorESM-OC1 and NorESM-OC1.2 is the addition of two new options for the treatment of sinking particles. We find that using a constant sinking speed, which has been the standard in NorESM's ocean carbon cycle module HAMOCC (HAMburg Ocean Carbon Cycle model) does not transport enough particulate organic carbon (POC) into the deep ocean below approximately 2000 m depth. The two newly implemented parameterisations, a particle aggregation scheme with prognostic sinking speed, and a simpler scheme prescribing a linear increase of sinking speed with depth, provide better agreement with observed POC fluxes. Additionally, reduced deep ocean biases of oxygen and remineralised phosphate indicate a better performance of the new parameterisations. For model version 1.2, a re-tuning of the ecosystem parameterisation has been performed, which (i) reduces previously too high primary production in high latitudes, (ii) consequently improves model results for surface nutrients, and (iii) reduces alkalinity and dissolved inorganic carbon biases at low latitudes. We use hindcast simulations with prescribed observed and constant (pre-industrial) atmospheric CO2 concentrations to derive the past and contemporary ocean carbon sink. For the period 1990–1999 we find an average ocean carbon uptake ranging from 2.01 to 2.58 Pg C yr-1 depending on model version, grid resolution and atmospheric forcing data set

Context matters when using climate model projections for aquaculture

At present, specific guidance on how to choose, assess and interpret climate model projections for the aquaculture sector is scarce. Since many aspects of aquaculture production are influenced by the local farm-level environment, there is a need to consider how climate model projections can be used to predict potential future farming conditions locally. This study compared in-situ measurements of temperature and salinity from Norwegian salmon farms and fixed monitoring stations to simulations from a regional ocean climate model for multiple locations and depths in southern Norway. For locations considered in this study, a similar seasonal cycle in terms of phasing was visible for modelled and measured temperatures. For some depths and times of the year the modelled and measured temperatures were similar, but for others there were differences. The model tended to underestimate temperature. On occasion there were differences between average modelled and measured temperatures of several degrees and aquaculture users would need to consider the implications of using the modelled temperatures. As for salinity, the model does not include localized freshwater inputs, so the model overestimated salinity for locations close to shore and was not able to represent more brackish water conditions in shallower depths. It was not possible to draw a general conclusion as to whether the model was suitable for aquaculture purposes, as the similarities and differences between the modelled and measured values varied by variable, area, depth, and time. These findings made it clear that aquaculture users would have to implement a process to determine whether they could use climate model outputs for their specific purpose. A model vetting framework is presented that can be used to support decisions on the use of climate model projections for aquaculture purposes. The vetting framework describes four stages that can be used to establish the necessary context regarding the aquaculture requirements and model capabilities, and then check how the model is simulating the conditions of interest at farm sites. Although the focus was aquaculture, the findings are relevant for other sectors and the framework can guide use of climate models for more local-scale assessment and management in coastal locations

Ocean biogeochemistry in the Norwegian Earth System Model version 2 (NorESM2)

The ocean carbon cycle is a key player in the climate system through its role in regulating the atmospheric carbon dioxide concentration and other processes that alter the Earth's radiative balance. In the second version of the Norwegian Earth System Model (NorESM2), the oceanic carbon cycle component has gone through numerous updates that include, amongst others, improved process representations, increased interactions with the atmosphere, and additional new tracers. Oceanic dimethyl sulfide (DMS) is now prognostically simulated and its fluxes are directly coupled with the atmospheric component, leading to a direct feedback to the climate. Atmospheric nitrogen deposition and additional riverine inputs of other biogeochemical tracers have recently been included in the model. The implementation of new tracers such as “preformed” and “natural” tracers enables a separation of physical from biogeochemical drivers as well as of internal from external forcings and hence a better diagnostic of the simulated biogeochemical variability. Carbon isotope tracers have been implemented and will be relevant for studying long-term past climate changes. Here, we describe these new model implementations and present an evaluation of the model's performance in simulating the observed climatological states of water-column biogeochemistry and in simulating transient evolution over the historical period. Compared to its predecessor NorESM1, the new model's performance has improved considerably in many aspects. In the interior, the observed spatial patterns of nutrients, oxygen, and carbon chemistry are better reproduced, reducing the overall model biases. A new set of ecosystem parameters and improved mixed layer dynamics improve the representation of upper-ocean processes (biological production and air–sea CO2 fluxes) at seasonal timescale. Transient warming and air–sea CO2 fluxes over the historical period are also in good agreement with observation-based estimates. NorESM2 participates in the Coupled Model Intercomparison Project phase 6 (CMIP6) through DECK (Diagnostic, Evaluation and Characterization of Klima) and several endorsed MIP simulations.publishedVersio

Context matters when using climate model projections for aquaculture

At present, specific guidance on how to choose, assess and interpret climate model projections for the aquaculture sector is scarce. Since many aspects of aquaculture production are influenced by the local farm-level environment, there is a need to consider how climate model projections can be used to predict potential future farming conditions locally. This study compared in-situ measurements of temperature and salinity from Norwegian salmon farms and fixed monitoring stations to simulations from a regional ocean climate model for multiple locations and depths in southern Norway. For locations considered in this study, a similar seasonal cycle in terms of phasing was visible for modelled and measured temperatures. For some depths and times of the year the modelled and measured temperatures were similar, but for others there were differences. The model tended to underestimate temperature. On occasion there were differences between average modelled and measured temperatures of several degrees and aquaculture users would need to consider the implications of using the modelled temperatures. As for salinity, the model does not include localized freshwater inputs, so the model overestimated salinity for locations close to shore and was not able to represent more brackish water conditions in shallower depths. It was not possible to draw a general conclusion as to whether the model was suitable for aquaculture purposes, as the similarities and differences between the modelled and measured values varied by variable, area, depth, and time. These findings made it clear that aquaculture users would have to implement a process to determine whether they could use climate model outputs for their specific purpose. A model vetting framework is presented that can be used to support decisions on the use of climate model projections for aquaculture purposes. The vetting framework describes four stages that can be used to establish the necessary context regarding the aquaculture requirements and model capabilities, and then check how the model is simulating the conditions of interest at farm sites. Although the focus was aquaculture, the findings are relevant for other sectors and the framework can guide use of climate models for more local-scale assessment and management in coastal locations

The impacts of ocean acidification on marine trace gases and the implications for atmospheric chemistry and climate

Surface ocean biogeochemistry and photochemistry regulate ocean–atmosphere fluxes of trace gases critical for Earth’s atmospheric chemistry and climate. The oceanic processes governing these fluxes are often sensitive to the changes in ocean pH (or pCO2) accompanying ocean acidification (OA), with potential for future climate feedbacks. Here, we review current understanding (from observational, experimental and model studies) on the impact of OA on marine sources of key climate-active trace gases, including dimethyl sulfide (DMS), nitrous oxide (N2O), ammonia and halocarbons. We focus on DMS, for which available information is considerably greater than for other trace gases. We highlight OA-sensitive regions such as polar oceans and upwelling systems, and discuss the combined effect of multiple climate stressors (ocean warming and deoxygenation) on trace gas fluxes. To unravel the biological mechanisms responsible for trace gas production, and to detect adaptation, we propose combining process rate measurements of trace gases with longer term experiments using both model organisms in the laboratory and natural planktonic communities in the field. Future ocean observations of trace gases should be routinely accompanied by measurements of two components of the carbonate system to improve our understanding of how in situ carbonate chemistry influences trace gas production. Together, this will lead to improvements in current process model capabilities and more reliable predictions of future global marine trace gas fluxes