Long internal gravity waves: dynamics and wave-induced transport
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ocean transport gravity waves turbulence↗
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Remote Sensing of Dissolved Organic Carbon (DOC) Stocks, Fluxes and Transformations Along the Land-Ocean Aquatic Continuum: Advances, Challenges, and Opportunities
From stream headwaters to the deep ocean, dissolved organic carbon (DOC) is ubiquitous across the world’s aquatic systems, where it fulfills important biogeochemical and ecological functions. Importantly, DOC is a dynamic component of the carbon cycle and represents a global reservoir (mass of carbon) equivalent in size to the atmospheric CO 2 pool. The mobilization, transport, and transformations of DOC along the land-ocean aquatic continuum biogeochemically connect terrestrial landscapes, freshwater and marine ecosystems, and the atmosphere. As a result, DOC budgets are vulnerable to climate- and human-driven changes. Having the ability to quantify DOC stocks, fluxes, and transformations consistently and accurately in aquatic systems is therefore critical to our understanding of DOC’s functions in the environment and of their susceptibility to change. In recent years, optical techniques and synoptic earth observations have opened new doors to achieve this objective on a global scale. In this review, we explore how in situ measurements, earth observations, and the models they inform have facilitated the large-scale quantification of DOC stocks, fluxes, and transformations along the land-ocean aquatic continuum and how they have helped shape our current understanding of the global DOC cycle. The review also explores the limitations and challenges we still face today, and discusses how upcoming and future sensors are expected to provide new insights into our understanding of the global DOC cycle.
Dissolved organic carbon↗
Further Studies on Oceanic Biogeochemistry and Carbon Cycling
This TM consists of two chapters. Chapter I describes the development of a coupled, one-dimensional biogeochemical model using turbulence closure mixed layer (TCMLM) dynamics. The model is applied to the Sargasso Sea at the BATS (Bermuda Atlantic Time Series) site and the results are compared with a previous model study in the same region described in NASNTP-2001-209991. The use of the TCMLM contributed to some improvements in the model simulation of chlorophyll, PAR, nitrate, phosphate, and oxygen, but most importantly, the current model achieved good agreement with the data with much more realistic background eddy diffusivity. However, off-line calculations of horizontal transport of biogeochemical properties revealed that one-dimensional dynamics can only provide a limited assessment of the nutrient and carbon balances at BATS. Future studies in the BATS region will require comprehensive three-dimensional field studies, combined with three-dimensional eddy resolving numerical experiments, to adequately quantify the impact of the local and remote forcing on ecosystem dynamics and carbon cycling. Chapter II addresses the sensitivity of global sea-air CO, flux estimates to wind speed, temperature, and salinity. Sensitivity analyses of sea-air CO, flux to wind speed climatologies, gas transfer algorithms, SSS and SST were conducted for the global oceans and regional domains. Large uncertainties in the global sea-air flux are identified, primarily due to the different gas transfer algorithms used. The sensitivity of the sea-air flux to SST and SSS is similar in magnitude to the effect of using different wind climatologies. Globally, the mean ocean uptake of CO, changes by 5 to 16%, depending upon the combination of SST and SSS used.
Signorini, S. R.↗
Stratospheric aerosol forcing for CMIP7 – Part 1: optical properties for pre-industrial, historical, and scenario simulations
Stratospheric aerosols, most of which originate from explosive volcanic sulfur emissions into the stratosphere, are a key natural driver of climate variability. They are thus a forcing provided by the Coupled Model Intercomparison Project (CMIP) Climate Forcings Task Team to climate modelling groups participating in phase 7 of CMIP. For the historical period, we provide two datasets covering 1750–2023: (i) a volcanic upper tropospheric-stratospheric sulfur emission dataset, documented in a companion paper; and (ii) a stratospheric sulfate aerosol optical property dataset, documented in this paper. For the satellite era (from 1979 onwards), stratospheric aerosol optical properties are derived from the Global Space-based Stratospheric Aerosol Climatology (GloSSAC) dataset. For the pre-satellite era (1750–1978), optical properties are derived from our volcanic SO 2 emission dataset using a new version of the reduced-complexity volcanic aerosol model Easy Volcanic Aerosol (Height) (EVA_H v2). A background, non-volcanic stratospheric aerosol climatology is derived from the 1998–2001 period with a trend over 1850–1978 accounting for increasing anthropogenic aerosols. A monthly stratospheric aerosol climatology is derived from the 1850–2021 average for both pre-industrial and scenario (future) simulations, with a 9-year ramp over 2022–2030 for scenario simulations to ensure a smooth transition from the historical period. CMIP7 simulations should use version 2.2.1 of our pre-industrial and historical datasets, and version 2.2.2 of our scenario dataset. Our methodology to produce historical aerosol optical properties significantly differs from CMIP6 for the pre-satellite era, and the resulting forcings in turn largely differ. In particular, the CMIP6 dataset was mostly based on the sparse and uncertain pyrheliometer record, which resulted in strongly underrepresented emissions from small-to-moderate magnitude eruptions. The resulting bias is addressed in CMIP7, which is entirely emission-derived in the pre-satellite era and uses more recent ice-core-based volcanic sulfur emission inventories than CMIP6. Our approach results in an overall larger volcanic aerosol forcing for CMIP7, with the 1850–2014 mean mid-visible global mean stratospheric aerosol optical depth (SAOD) in CMIP7 (0.0138) being 29 % higher than in CMIP6 (0.0107). The pre-industrial mean of the same variable is 26 % higher in CMIP7 (0.0135, derived from the historical 1850–2021) than CMIP6 (0.0107, derived from the historical 1850–2014 mean). Using a reduced-complexity climate model, we simulate a global mean surface temperature that is 0.07 °C colder for 1850–1900 when using the CMIP7 dataset instead of CMIP6, whereas 2000–2014 is 0.03 °C warmer in CMIP7. Our dataset also exhibits lower forcing for 1960–1980, resulting in temperatures 0.06 °C warmer when averaged over 1960–1990, a period for which CMIP6 climate models exhibit a cold bias. Given the large uncertainties characterizing the dataset, in particular for the pre-satellite era, we advise against treating the CMIP7 or CMIP6 dataset as uniquely superior for any specific year and highlight the need for further evaluation. We conclude the study by discussing sources of uncertainty for the dataset, future research avenues to improve it, as well as requirements to operationalize the production of the dataset, i.e. extend it and update it on an annual basis instead of every 5–7 years following CMIP cycles.
Aubry, Thomas J. [Univ. of Exeter, Devon (United K↗
Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network
Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. Knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to improve understanding of atmospheric sulfur, aerosol/cloud dynamics, and albedo. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 82 996 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of latitude–longitude coordinates, time of day, time of year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, and silicate. Linear regressions of DMS against the environmental parameters show that on a global-scale mixed layer depth and solar radiation are the strongest predictors of DMS. These parameters capture ~9 % and ~7 % of the raw DMS data variance, respectively. Multilinear regression can capture more of the raw data variance (~39 %) but strongly underestimates DMS in high-concentration regions. In contrast, the artificial neural network captures ~66 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in surface ocean DMS with the highest concentrations and sea-to-air fluxes in the high-latitude summertime oceans. We estimate a lower global sea-to-air DMS flux (20.12±0.43 Tg S yr –1 ) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used. Our sensitivity test results show that DMS concentration does not change unidirectionally with each of the environmental parameters, which emphasizes the interactions among these parameters. The ANN model suggests that the flux of DMS from the ocean to the atmosphere will increase with global warming. Given that larger DMS fluxes induce greater cloud albedo, this corresponds to a negative climate feedback.
54 ENVIRONMENTAL SCIENCES↗
The hazard components of representative key risks. The physical climate perspective
The framework of Representative Key Risks (RKRs) has been adopted by the Intergovernmental Panel on Climate Change Working Group II in order to categorize, assess and communicate a wide range of regional and sectoral key risks, expected to become severe as a consequence of the potentially detrimental convergence of changing climate conditions, exposure and vulnerability of human and natural systems, and responses through (mal)adaptation measures. Other papers in this special issue treat each of eight RKRs by holistically assessing their current status and future evolution as a result of this convergence. However, their treatment cannot always organize such assessment according to a systematic gradation of climatic changes, often times having to extrapolate the big-picture evolution of risk from either qualitative effects of “low” medium” and “high” warming, or limited/focused analysis of the consequences of particular mitigation choices (e.g., benefits of limiting warming to 1.5 or 2C). In this study we offer as systematic a representation as possible – given current literature and assessments – of the future evolution of the hazard components of RKRs. We draw on the assessment of changes in climatic impact-drivers relevant to RKRs described in the 6th Assessment Report by Working Group 1 (WGI) supplemented when needed by more recent literature. We identify the relevant hazards for each RKR, based upon the WGII authors’ assessment, and we report on their current state and expected future changes in magnitude, intensity and/or frequency, linking these changes to Global Warming Levels to the extent possible. For some of these quantities - like regional trends in oceanic and atmospheric temperature and precipitation, some heat and precipitation extremes, permafrost thaw and Northern Hemisphere snow cover - a strong and quantitative relationship with global average temperature change has been assessed. For others - like frequency and intensity of tropical cyclones and extra-tropical storms, and fire weather - the link to increasing global temperatures can only be described qualitatively. For some processes - like the behavior of ice-sheets, or changes in circulation dynamics - large uncertainties about the effects of different levels of warming remain, and for a few others - like ocean pH and air pollution - the composition of the scenario of anthropogenic emissions is most relevant, rather than the warming reached. In almost all cases, however, the basic message remains that every small increment in CO2 concentration in the atmosphere and associated warming will bring changes in climate phenomena that will contribute to increasing risk of impacts on human and natural systems when these changes happen in conjunction with these systems’ conditions of exposure and vulnerability, and in lack of effective adaptation. Our picture of the evolution of RKR-relevant climatic impact-drivers complements and enriches the treatment of RKRs in the other papers in at least two ways: by filling in an often only cursory or limited representation of the physical climate aspects driving impacts, and by providing a fuller representation of their future possible evolution which represents an important component – if never the only one – of the potential future evolution of risk severity.
54 ENVIRONMENTAL SCIENCES↗
Global Carbon Budget 2015
Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We also discuss changes compared to previous estimates as well as consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuels and industry ( E FF ) are based on energy statistics and cement production data, while emissions from land-use change ( E LUC ), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth ( G ATM ) is computed from the annual changes in concentration. Moreover, the mean ocean CO 2 sink ( S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink ( S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO 2 , and land-cover change (some including nitrogen–carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2005–2014), E FF was 9.0 ± 0.5 GtC yr -1 , E LUC was 0.9 ± 0.5 GtC yr -1 , G ATM was 4.4 ± 0.1 GtC yr -1 , S OCEAN was 2.6 ± 0.5 GtC yr -1 , and S LAND was 3.0 ± 0.8 GtC yr -1 . For the year 2014 alone, E FF grew to 9.8 ± 0.5 GtC yr -1 , 0.6 % above 2013, continuing the growth trend in these emissions, albeit at a slower rate compared to the average growth of 2.2 % yr -1 that took place during 2005–2014. Also, for 2014, E LUC was 1.1 ± 0.5 GtC yr -1 , G ATM was 3.9 ± 0.2 GtC yr -1 , S OCEAN was 2.9 ± 0.5 GtC yr -1 , and S LAND was 4.1 ± 0.9 GtC yr -1 . G ATM was lower in 2014 compared to the past decade (2005–2014), reflecting a larger S LAND for that year. The global atmospheric CO 2 concentration reached 397.15 ± 0.10 ppm averaged over 2014. For 2015, preliminary data indicate that the growth in E FF will be near or slightly below zero, with a projection of -0.6 [range of -1.6 to +0.5] %, based on national emissions projections for China and the USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the global economy for the rest of the world. From this projection of E FF and assumed constant E LUC for 2015, cumulative emissions of CO 2 will reach about 555 ± 55 GtC (2035 ± 205 GtCO 2 ) for 1870–2015, about 75 % from E FF and 25 % from E LUC . Finally, this living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center ( doi:10.3334/CDIAC/GCP_2015 ).
54 ENVIRONMENTAL SCIENCES↗
Global carbon budget 2014
Accurate assessment of anthropogenic carbon dioxide (CO 2 ) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates, consistency within and among components, alongside methodology and data limitations. CO 2 emissions from fossil fuel combustion and cement production ( E FF ) are based on energy statistics and cement production data, respectively, while emissions from land-use change ( E LUC ), mainly deforestation, are based on combined evidence from land-cover-change data, fire activity associated with deforestation, and models. The global atmospheric CO 2 concentration is measured directly and its rate of growth ( G ATM ) is computed from the annual changes in concentration. The mean ocean CO 2 sink ( S OCEAN ) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in S OCEAN is evaluated with data products based on surveys of ocean CO 2 measurements. The global residual terrestrial CO 2 sink ( S LAND ) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models forced by observed climate, CO 2 , and land-cover-change (some including nitrogen–carbon interactions). We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ;, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2004–2013), E FF was 8.9 ± 0.4 GtC yr⁻¹, E LUC 0.9 ± 0.5 GtC yr⁻¹, G ATM 4.3 ± 0.1 GtC yr⁻¹, S OCEAN 2.6 ± 0.5 GtC yr⁻¹, and S LAND 2.9 ± 0.8 GtC yr⁻¹. For year 2013 alone, E FF grew to 9.9 ± 0.5 GtC yr⁻¹, 2.3% above 2012, continuing the growth trend in these emissions, E LUC was 0.9 ± 0.5 GtC yr⁻¹, G ATM was 5.4 ± 0.2 GtC yr⁻¹, S OCEAN was 2.9 ± 0.5 GtC yr⁻¹, and S LAND was 2.5 ± 0.9 GtC yr⁻¹. G ATM was high in 2013, reflecting a steady increase in E FF and smaller and opposite changes between S OCEAN and S LAND compared to the past decade (2004–2013). The global atmospheric CO 2 concentration reached 395.31 ± 0.10 ppm averaged over 2013. We estimate that E FF will increase by 2.5% (1.3–3.5%) to 10.1 ± 0.6 GtC in 2014 (37.0 ± 2.2 GtCO 2 yr⁻¹), 65% above emissions in 1990, based on projections of world gross domestic product and recent changes in the carbon intensity of the global economy. From this projection of E FF and assumed constant E LUC for 2014, cumulative emissions of CO 2 will reach about 545 ± 55 GtC (2000 ± 200 GtCO 2 ) for 1870–2014, about 75% from E FF and 25% from E LUC . This paper documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this living data set (Le Quéré et al., 2013, 2014). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center ( doi:10.3334/CDIAC/GCP_2014 ).
54 ENVIRONMENTAL SCIENCES↗
Natural Aerosols Explain Seasonal and Spatial Patterns of Southern Ocean Cloud Albedo
Small particles called aerosols act as nucleation sites for cloud drop formation, affecting clouds and cloud properties – ultimately influencing the cloud dynamics, lifetime, water path and areal extent that determine the reflectivity (albedo) of clouds. The concentration Nd of droplets in clouds that influences planetary albedo is sensitive to the availability of aerosol particles on which the droplets form. Natural aerosol concentrations not only affect cloud properties themselves, but also modulate the sensitivity of clouds to changes in anthropogenic aerosols. Here, it is shown that modeled natural aerosols, principally marine biogenic primary and secondary aerosol sources, explain more than half of the spatiotemporal variability in satellite-observed Nd. Enhanced Nd over regions of high biological activity is found to be driven primarily by high concentrations of sulfate aerosol at lower Southern Ocean latitudes (35-45°S) and by organic matter in sea spray aerosol at higher latitudes (45-55°S). Biogenic sources are estimated to increase the summertime mean reflected solar radiation in excess of 10 W m-2 over parts of the Southern Ocean, which is comparable to the annual mean increases expected from anthropogenic aerosols over heavily polluted regions of the Northern Hemisphere.
cloud droplet concentration↗
On the wave number spectrum of oceanic mesoscale variability observed by the SEASAT altimeter
The wave number spectrum of mesoscale variability in various parts of the oceans is computed on the basis of sea surface height variations measured by the SEASAT altimeter in nearly repeat orbits during the last 24 days of its mission, and it is noted that oceanic spectrum characteristics are dependent on the energy level of the mesoscale variability. On the basis of an assumed horizontal isotropy of mesoscale variability, scalar-wave number spectra of sea surface height and geostrophic kinetic energy are presented and the dynamical implications of these spectra are discussed. A rigorous examination of the effects of residual geoid and atmospheric water vapor on the computed oceanic spectra indicates that the general characteristics of the spectra were not significantly affected by them.
Fu, L.-L.↗
Comparison of dynamic height variations in the tropical Atlantic during 1987-1989 as viewed in sections hydrography and Geosat altimetry
During the 1980s the Soviet Union conducted a series of 25 hydrographic cruises in the tropical Atlantic under the Sections (acronym for Energetically Active Zones of the Ocean and Climate Variability) program. As one of its objectives, Sections has produced the most extensive hydrographic data set ever collected in the tropical Atlantic. Here we examine dynamic topography from seven cruises during the years 1987 through 1989 and compare them to corresponding maps of sea level from the Geosat satellite mission. The two independent estimates of sea level variation are well correlated, with the sea level fields explaining approximately half of the variance of dynamic height fields. There is a seasonal variation to the agreement, so that the correlation increases somewhat in summer and fall, as the variability of the height field increases. Wavenumber spectra of both dynamic height and sea level are quite red for wavelengths greater than 400-600 km. Smaller wavelengths cannot be resolved by the hydrographic sampling program. A comparison of the dynamic height and sea level spectra indicates that sea level has somewhat less variability than dynamic height at all wavenumbers.
Carton, James A.↗
Collaborative Research: Atlantic Ocean Tropical/Subtropical Processes from Seasonal to Decadal Time Scales: Model/Data, Model/Model Comparison and Model/Data Synthesis Through Assimilation
The effort of this first year of research has been focused on the assimilation of TOPEX/Poseidon altimetric data into a primitive equation model of the Atlantic tropical/subtropical circulation. A reduced-rank, stationary Kalman filter has been constructed to assimilate the altimetric sea surface height anomaly (SHA) into the model. The goal is to assess how the inter-hemispheric transports between the Atlantic subtropics and tropics are affected by the assimilation and how the subsurface thermocline structure , and its variability ,is dynamically constrained by the SHA. The model is a reduced-gravity primitive equation GCM of the upper Atlantic Ocean between 30 S and 30 N. The assimilation scheme is an approximation to the extended Kalman filter in which the error covariances of the state estimates are calculated only in a reduced- dimension subspace. The subspace is defined by the leading empirical orthogonal functions calculated from an unconstrained model calculation. Both an identical twin experiment using simulated SHA observations and assimilation of the real TOPEX data were performed. Results from the twin experiments demonstrate the ability of the method to constrain the ocean circulation and the subsurface temperature structure. The impact on the subsurface temperature structure of TOPEX assimilation was assessed using data from expandable bathythermographs. This showed a substantial improvement in the estimated temperature variability only within 13 degrees in latitude around the equator. The impact of TOPEX SHA assimilation on zonally integrated meridional transport across different latitudes was also estimated. Again within 13 degrees from the equator both the mean amplitude and interannual variability of the surface and subsurface transports were significantly enhanced, while the transports were insensitive to the assimilation in the subtropics.
Malannotte-Rizzoli, Paola↗
Comparison of Conventional and Constrained Variational Methods for Computing Large-Scale Budgets and Forcing Fields
Analyses of atmospheric heat and moisture budgets serve as an effective tool to study convective characteristics over a region and to provide large-scale forcing fields for various modeling applications. This paper examines two popular methods for computing large-scale atmospheric budgets: the conventional budget method (CBM) using objectively gridded analyses based primarily on radiosonde data and the constrained variational analysis (CVA) approach which supplements vertical profiles of atmospheric fields with measurements at the top of the atmosphere and at the surface to conserve mass, water, energy, and momentum. Successful budget computations are dependent on accurate sampling and analyses of the thermodynamic state of the atmosphere and the divergence field associated with convection and the large-scale circulation that influences it. Utilizing analyses generated from data taken during Dynamics of the Madden-Julian Oscillation (DYNAMO) field campaign conducted over the central Indian Ocean from October to December 2011, we evaluate the merits of these budget approaches and examine their limitations. While many of the shortcomings of the CBM, in particular effects of sampling errors in sounding data, are effectively minimized with CVA, accurate large-scale diagnostics in CVA are dependent on reliable background fields and rainfall constraints. Finally, for the DYNAMO analyses examined, the operational model fields used as the CVA background state provided wind fields that accurately resolved the vertical structure of convection in the vicinity of Gan Island. However, biases in the model thermodynamic fields were somewhat amplified in CVA resulting in a convective environment much weaker than observed.
54 ENVIRONMENTAL SCIENCES↗
Oscillations in a simple climate–vegetation model
We formulate and analyze a simple dynamical systems model for climate–vegetation interaction. The planet we consider consists of a large ocean and a land surface on which vegetation can grow. The temperature affects vegetation growth on land and the amount of sea ice on the ocean. Conversely, vegetation and sea ice change the albedo of the planet, which in turn changes its energy balance and hence the temperature evolution. Our highly idealized, conceptual model is governed by two nonlinear, coupled ordinary differential equations, one for global temperature, the other for vegetation cover. The model exhibits either bistability between a vegetated and a desert state or oscillatory behavior. The oscillations arise through a Hopf bifurcation off the vegetated state, when the death rate of vegetation is low enough. These oscillations are anharmonic and exhibit a sawtooth shape that is characteristic of relaxation oscillations, as well as suggestive of the sharp deglaciations of the Quaternary. Our model's behavior can be compared, on the one hand, with the bistability of even simpler, Daisyworld-style climate–vegetation models. On the other hand, it can be integrated into the hierarchy of models trying to simulate and explain oscillatory behavior in the climate system. Rigorous mathematical results are obtained that link the nature of the feedbacks with the nature and the stability of the solutions. The relevance of model results to climate variability on various timescales is discussed.
54 ENVIRONMENTAL SCIENCES↗
The Lightning Imaging Sensor
The Lightning Imaging Sensor (LIS) is a NASA Earth Observing System (EOS) instrument on the Tropical Rainfall Measuring Mission (TRMM) platform designed to acquire and investigate the distribution and variability of total lightning (i.e., cloud-to-ground and intracloud) between q35' in latitude. Since lightning is one of the responses of the atmosphere to thermodynamic and dynamic forcing, the LIS data is being used to detect deep convection without land-ocean bias, estimate the precipitation mass in the mixed phased region of thunderclouds, and differentiate storms with strong updrafts from those with weak vertical motion.
Christian, Hugh J.↗
Time-Variable Gravity Signal Due to Extratropic Pacific Water Mass Redistribution
Cox and Chao [2002] reported the detection of a large anomaly in the form of a positive "jump" in the time series of Earth's lowest-degree gravity harmonic J2, or the dynamic oblateness, during 1998. This prompted us to examine the mass redistribution in the global oceans. We report here a seesaw of the sea-surface height (SSH) in the extratropic north + south Pacific basins -- the leading (nonseasonal) EOF/PC mode in SSH derived from the 10-year TOPEX/Poseidon altimetry data in the extratropic Pacific region. The mode underwent a step-like jump with time evolution that match remarkably well with the observed J2 anomaly. However, the magnitude is several times too small to explain the observed J2, even if assuming the SSH jump was all mass-induced (as opposed to any steric effect which causes no time-variable gravity signal). If one accepts the notion that this extratropic Pacific seesaw is part of the geophysical process that produced the observed 1998 J2 anomaly, then this finding suggests strong geophysical connection of the interannual-to-decadal variation of J2 with the Pacific Decadal Oscillation (PDO), as the time series of the above EOF/PC mode is actually a formally defined PDO Index series.
Chao, Benjamin F.↗
GEWEX: The Global Energy and Water Cycle Experiment
GEWEX is one of the world's largest global change research programs. Its purpose is to observe and understand the hydrological cycle and energy fluxes in the atmosphere, at land surfaces and in the upper oceans.
GEWEX global hydrological regimes surface dynamics↗
Hybrid Energy-Powered Electrochemical Direct Ocean Capture Model
Offshore synthetic fuel production and marine carbon dioxide removal can be enabled by direct ocean capture, which extracts carbon dioxide from the ocean that then can be used as a feedstock for fuel production or sequestered underground. To maximize carbon capture, plants require a variety of low-carbon energy sources to operate, such as variable renewable energy. However, the impacts of variable power on direct ocean capture have not yet been thoroughly investigated. To facilitate future deployments, a generalizable model for electrodialysis-based direct ocean capture plants is created to evaluate plant performance and electricity costs under intermittent power availability. This open-source Python-based model captures key aspects of the electrochemistry, ocean chemistry, post-processing, and operation scenarios under various conditions. To incorporate realistic energy supply dynamics and cost estimates, the model is coupled with the National Renewable Energy Laboratory’s H2Integrate tool, which simulates hybrid energy system performance profiles and costs. This integrated framework is designed to provide system-level insights while maintaining computational efficiency and flexibility for scenario exploration. Initial evaluations show similar results to those predicted by the industry, and demonstrate how a given plant could function with variable power in different deployment locations, such as with wind energy off the coast of Texas and with wind and wave energy off the coast of Oregon. The results suggest that electrochemical systems with greater tolerances for power variability and low minimum power requirements may offer operational advantages in variable-energy contexts. However, further research is needed to quantify these benefits and evaluate their implications across different deployment scenarios.
16 TIDAL AND WAVE POWER↗