Stratospheric aerosol injection (SAI) has been shown in climate models to reduce some impacts of global warming in the Arctic, including the loss of sea ice, permafrost thaw, and reduction of Greenland Ice Sheet (GrIS) mass; SAI at high latitudes could preferentially target these impacts. In this study, we use the Community Earth System Model to simulate two Arctic-focused SAI strategies, which inject at 60°N latitude each spring with injection rates adjusted to either maintain September Arctic sea ice at 2030 levels (“Arctic Low”) or restore it to 2010 levels (“Arctic High”). Both simulations maintain or restore September Arctic sea ice to within 10% of their respective targets, reduce permafrost thaw, and increase GrIS surface mass balance by reducing runoff. Arctic High reduces these impacts more effectively than a globally-focused SAI strategy that injects similar quantities of SO2 at lower latitudes. However, Arctic-focused SAI is not merely a “reset button” for the Arctic climate, but brings about a novel climate state, including changes to the seasonal cycles of Northern Hemisphere temperature and sea ice and less high-latitude carbon uptake relative to SSP2-4.5. Additionally, while Arctic-focused SAI predominantly cools the Arctic, its effects are not confined to the Arctic, including detectable cooling throughout most of the northern hemisphere for both simulations, increased mid-latitude sulfur deposition, and a southward shift of the location of the Intertropical Convergence Zone (ITCZ).

This study investigates the occurrence of mixed-phase clouds (MPC) over the Southern Ocean (SO) using space- and surface-based lidar and radar observations. The occurrence of supercooled clouds is dominated by geometrically thin (< 1km) layers that are rarely MPC. We diagnose layers that are geometrically thicker than 1 km to be MPC approximately 65%, and 4% of the time from below by surface remote sensors and from above by orbiting remote sensors, respectively. We examine the discrepancy in MPC as diagnosed from the below and above. From above, we find that MPC occurrence has a gradient associated with the Antarctic Polar Front near 55°S with the rare occurrence of satellite-derived MPC south of that latitude. In contrast, surface sensors find MPC in 33% of supercooled layers. We infer that space-based lidar cannot identify the occurrence of MPC except when secondary ice-forming processes operate in convection that is sufficiently strong to loft ice crystals to cloud tops. We conclude that the CALIPSO phase statistics of MPC have a severe low bias in MPC occurrence. Based on surface-based statistics, we present a parameterization of the frequency of MPC as a function of cloud top temperature that differs substantially from that used in recent climate model simulations.

Natural and non-natural factors have combined effects on the trajectory of COVID-19 pandemic, but it is difficult to make them separate. To address this problem, a two-stepped methodology is proposed. First, a compound natural factor (CNF) model is developed via assigning weight to each of seven investigated natural factors, i.e., temperature, humidity, visibility, wind speed, barometric pressure, aerosol and vegetation in order to show their coupling relationship with the COVID-19 trajectory. Onward, the empirical distribution based framework (EDBF) is employed to iteratively optimize the coupling relationship between trajectory and CNF to express the real interaction. In addition, the collected data is considered from the backdate, i.e., about 23 days—which contains 14-days incubation period and 9-days invalid human response time—due to the non-availability of prior information about the natural spreading of virus without any human intervention(s), and also lag effects of the weather change and social interventions on the observed trajectory due to the COVID-19 incubation period; Second, the optimized CNF-plus-polynomial model is used to predict the future trajectory of COVID-19.Results revealed that aerosol and visibility show the higher contribution to transmission, wind speed to death, and humidity followed by barometric pressure dominate the recovery rates, respectively. Consequently, the average effect of environmental change to COVID-19 trajectory in China is minor in all variables, i.e., about -0.3%, +0.3% and +0.1%, respectively. In this research, the response analysis of COVID-19 trajectory to the compound natural interactions presents a new prospect on the part of global pandemic trajectory to environmental changes.

Many of the respiratory pathogens show seasonal patterns and association with environmental factors. In this article, we conducted a cross-sectional analysis of the influence of environmental factors, including climate change along with development indicators on the differential global spread and fatality of COVID-19 during its early phase. We used the published COVID-19 data by the WHO for April. Global climate data we used are monthly averaged gridded datasets of Temperature, Humidity and Temperature Anomaly. We used the HDI to account for all other socioeconomic factors that can affect the disease spread and mortality and build a negative binomial regression model. The temperature has a negative association with COVID-19 mortality. However, HDI is shown to confound the effect of temperature on the reporting of the disease. Temperature anomaly, which is being regarded as a global warming indicator, is positively associated with the pandemic's spread and mortality. Viewing newer infectious diseases like SARS-CoV-2 in the perspective of climate change has a lot of public health implications, and it necessitates further research.

The study was aimed to identify the relations between the severity of coronary artery disease and associated percutaneous coronary interventions with the changes in the local Earth magnetic field activity (LEMF). One-thousand-two-hundred-forty patients diagnosed with Acute coronary syndrome who underwent percutaneous coronary intervention within 2015-2016 were retrospectively included in this single centre study. The majority of acute coronary syndromes that occurred in females was associated with an increase in LEMF intensity in 3.5-32 Hz frequency ranges and were also associated with a higher number of diseased coronary arteries. Increased intensity in the same range was associated with a lower number of stented coronary arteries in males in 2015. Positive correlation coefficients were found between increased LEMF intensity in the 0-15 Hz range and the number of revascularized coronary arteries in females during the winter season in 2016. Stronger LEMF in low-medium frequency ranges is associated with acute coronary syndromes in males caused by less diffuse coronary artery disease resulting in lower number of coronary arteries segments needed for revascularisation, especially during winter. Stronger LEMF in high frequency range is associated with increased occurrence of ischaemic cardiovascular events, while stronger LEMF in low to moderate frequency ranges is associated with positive effect.

The maximum extent of the last North American ice sheet is well constrained empirically, but it has proven to be challenging to simulate with coupled Climate-Ice Sheet models. Coupled Climate-Ice Sheet models are often too computationally expensive to sufficiently explore uncertainty in input parameters, and it is unlikely values calibrated to reproduce modern ice sheets will reproduce the known extent of the ice at the Last Glacial Maximum. To address this, we run a series of ensembles with a coupled Climate-Ice Sheet model (FAMOUS-ice), simulating the final stages of growth of the last North American Ice Sheets’ maximum extent. Using this large ensemble approach, we explore the influence of uncertain ice sheet, albedo, atmospheric, and oceanic parameters on the ice sheet extent. We find that albedo parameters determine the majority of uncertainty when simulating the Last Glacial Maximum North American Ice Sheets. Importantly, different albedo parameters are needed to produce a good match to the Last Glacial Maximum North American Ice Sheets than have previously been used to model the contemporary Greenland Ice Sheet, due to differences in cloud cover over ablation zones. Thus calibrating coupled climate-ice sheet models solely for present day strongly biases simulations of past and future climates different from today.

An atmospheric composition feedback mechanism modulates the global equilibrium climate sensitivity (ECS) through changes in the tropical upper-tropospheric and lower-stratospheric (UTLS) water vapor. The feedback mechanism is caused by the acceleration of the Brewer-Dobson circulation. This process changes the ozone (O$_{3}$) concentration, resulting in a drier and cooler UTLS region than without O$_{3}$ changes. Thus, the planetary long-wave emissivity increases, and the ECS decreases. However, the BDC alone already provides dynamical cooling through the tropical stratospheric upwelling, potentially impacting the ECS. Here, we analyze the implications of this effect from a tropical clear-sky perspective, applying a one-dimensional radiative-convective equilibrium (RCE) model that explicitly accounts for the adiabatic cooling by the BDC and includes an interactive representation of O$_{3}$. We study how increasing upwelling modifies the change of the tropical energy budget resulting from a doubling of CO$_{2}$. An increase in upwelling reduces the tropical ECS mainly through an increased tropical energy export related to the adiabatic cooling. The atmospheric composition feedback through O$_{3}$ contributes less than 30\% to the tropical ECS reduction. Due to the dominance of the energy export, any impact on the global ECS will depend on the redistribution of the energy in the extratropics. We show that GCMs simulate similar changes of the tropical energy export under increased upwelling which corroborates that the findings obtained with the RCE approach bear relevance for the global climate.

Instrumental records of mean annual temperature extend back to the seventeenth and eighteenth centuries at multiple sites in Europe. For such long time series, we expect and find that histograms of mean annual temperature data become skewed towards higher temperatures with time because of global warming. However, we also find that skewness changed abruptly and started increasing between 1985 and 1991 (95% confidence) at 17 sites. We argue that this finding may imply an abrupt change in the climate system affecting Europe which probably occurred at this time. One possible cause is a climate tipping point having been passed. Of known tipping elements, we find Arctic sea ice loss, potentially linked to reduced sulfate aerosol emissions and coupled to temperature by an albedo feedback mechanism, a likely candidate. This is based on good correlations of sea ice extent and sulfate aerosol emissions with skewness of mean annual temperature data.

A heat danger day is defined as an extreme when the heat stress index (a combined temperature and humidity measure) exceeding 41 ℃, warranting public heat alerts. This study assesses future heat risk (i.e., heat danger days times the population at risk) based on the latest Coupled Model Intercomparison Project phase 6 (CMIP6) projections. In recent decades (1995-2014) China’s urban agglomerations (Beijing-Tianjin-Hebei, Yangtze River Delta, Middle Yangtze River, Chongqing-Chengdu, and Pearl River Delta) experienced no more than 3 heat danger days per year, but this number is projected to increase to 3-13 days during the population explosion period (2041-2060) under the high-emission pathways (SSP3-7.0 and SSP5-8.5). This increase will result in approximately 260 million people in these agglomerations facing more than 3 heat danger days annually, accounting for 19% of the total population of China, and will double the current level of overall heat risk. During the period 2081-2100, there will be 8-67 heat danger days per year, 60-90% of the urban agglomerations will exceed the current baseline number, and nearly 310 million people (39% of the total China population) will be exposed to the danger, with the overall heat risk exceeding 18 times the present level. The greatest risk is projected in the Pearl River Delta region with 67 heat danger days to occur annually under SSP5-8.5. With 65 million people (68% of the total population) experiencing increased heat danger days, the overall heat risk in the region will swell by a factor of 50. Conversely, under the low-emission pathways (SSP1-2.6 and SSP2-4.5), the annual heat danger days will remain similar to the present level or increase slightly. The result indicates the need to develop strategic plans to avoid the increased heat risk of urban agglomerations under high emission-population pathways.

The ocean plays a critical role in modulating climate change by sequestering CO2 from the atmosphere. Quantifying the CO2 flux across the air-sea interface requires time-dependent maps of surface ocean partial pressure of CO2 (pCO2), which can be estimated using global ocean biogeochemical models (GOBMs) and observational-based data products. GOBMs are internally consistent, mechanistic representations of the ocean circulation and carbon cycle, and have long been the standard for making spatio-temporally resolved estimates of air-sea CO2 fluxes. However, there are concerns about the fidelity of GOBM flux estimates. Observation-based products have the strength of being data-based, but the underlying data are sparse and require significant extrapolation to create global full-coverage flux estimates. The Lamont Doherty Earth Observatory-Hybrid Physics Data (LDEO-HPD) pCO2 product is a new approach to estimating the temporal evolution of surface ocean pCO2 and air-sea CO2 exchange. LDEO-HPD uses machine learning to merge high-quality observations with state-of-the-art GOBMs. We train an eXtreme Gradient Boosting (XGB) algorithm to learn a non-linear relationship between model-data mismatch and observed predictors. GOBM fields are then corrected with the predicted model-data misfit to estimate real-world pCO2 for 1982-2018. A benefit of this approach is that model-data misfit has reduced temporal skewness compared to the observed pCO2 that is the target variable for other machine-learning based reconstructions. This supports a robust reconstruction by LDEO-HPD that is in better agreement with independent observations than other estimates. LDEO-HPD global ocean uptake of CO2 is in agreement with other products and the Global Carbon Budget 2020.

The variability of streams in the atmosphere and the ocean, as shown in a number of studies, affects the change in the speed of the Earth’s rotation. However, it can cause a reverse reaction—a change in the Coriolis force; as a result of this, atmospheric and oceanic streams can have some variability. In the following work, a hypothesis is presented and considered: it suggests that a change in the volume of Atlantic water inflow into the Barents Sea is related to the change in the Earth’s rotation speed. The paper presents a methodology for determining representative values of the temperature and salinity of seawater that describe the largest possible volume of the sea, as well as a methodology for calculating the content of Atlantic, river and melt water for the period of 100 years. The change of these parameters, and the length of day values, demonstrates the presence of both linear trends and cyclical fluctuations with a period of about 80 years. As a result, it was shown that a decrease in the Earth’s rotation speed with a linear trend somewhat decreases the observed intensity of the processes of global climate change in the Arctic region (an increase in temperature and salinity). Due to the summation of positive anomalies, both a linear trend and a quasi-80-year cycle, the modern period is characterized by abnormally high values of water temperature, the growth of which has not stopped and will possibly reach its maximum between 2025 and 2030.

Volcanic flood basalt eruptions have covered 1000s of km2 with basalt deposits up to kilometers thick. The massive size and extended duration result in enormous releases of climactically-relevant gases such as SO2 and CO2. However, it is still unknown precisely how flood basalt eruptions influence climate via eruption rates and cadence, height of the volcanic plumes, and relative degassing abundance of species like SO2. Once eruptions occur, the complex interplay of photochemistry, greenhouse gas warming, changes to the atmospheric circulation, and aerosol-cloud interactions can only be properly simulated with a comprehensive global climate model (GCM). We created an eruption scenario for the Goddard Chemistry Climate Model (GEOSCCM) that emits SO2 in the near-surface atmosphere constantly and four times per year an explosive eruption that emits much more SO2 in the upper troposphere/lower stratosphere. The eruption lasts for 4 years and emits 30 Gt of SO2 total. This corresponds to ~1/10th of what may have been emitted during the Wapshilla Ridge eruption phase of the Columbia River flood basalt eruption 15-17 Ma. We use a pre-industrial atmosphere and otherwise modern initial and boundary conditions. The massive flux of SO2 into the atmosphere is quickly converted to H2SO4 aerosols. Global area-weighted mean visible band sulfate aerosol optical depth reaches 220 near the end of the eruption, comparable to cumulonimbus clouds. This reduces the surface shortwave radiative flux by 85% and top-of-atmosphere outgoing longwave flux by 70%. Contrary to our expectations, we find that the climate warms during and immediately following the eruption after a very brief initial cooling. Global mean surface temperature peaks 3-4 years after the eruption ends with a +6 K anomaly relative to a baseline simulation without the eruption. Post-eruption regional temperatures, particularly near-equatorial continental areas, see drastic rises of summertime temperatures with monthly mean temperatures equaling or exceeding 40°C. These temperature responses are radiative- and circulation-driven. The eruption warms and raises the tropical tropopause, allowing a massive flux of water vapor into the stratosphere. Stratospheric water vapor, usually ~3 parts per million reaches 1-2 parts per thousand.

A reduction of dust emission over the major dust source regions in East Asia in the twenty-first century is diagnosed in the climate change simulations of the Sixth Climate Model Intercomparison Project (CMIP6). Such change is attributable to the reduction of surface wind speeds in the dust source regions. To evaluate how the magnitude of warming affects dust emission, we examined two model scenarios, one high-forcing pathway and one medium-forcing pathway. We find dust optical depth over dust source regions would decrease by 5.6% by the end of the twenty-first century under the high-forcing pathway. Under the medium-forcing pathway, dust optical depth would decrease by less than 2%. These results provide a quantitative understanding of how global warming affects dust emission in the major dust source regions in East Asia.

Methane’s high heat trapping potential has made it a priority for quantification and mitigation efforts worldwide. Ground-based surveys and in-situ measurement techniques to quantify natural and fugitive methane emission sources are time-consuming, expensive, and often lead to sparse measurements. Failure to accurately quantify emissions at the point-source scale have thus led to poorly constrained emission estimates. Airborne imaging spectrometers such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and the Global Airborne Observatory (GAO) have been employed to map the often stochastic and intermittent point-source emissions from a diverse set of source types including oil and gas, dairy, etc. A matched filter is applied to the methane-absorption relevant spectral features of the instrument’s radiance cube. Machine learning models are then trained to recognize methane plumes from these column-matched filter methane maps. However, current Convolutional Neural Network (CNN) models suffer from a high false-positive rate and poorly generalize to new scenes. False-positive detections are primarily due to methane absorption-mimicking surface spectroscopic features, as well as a lack of training data. To supplement the available training data, we utilize Large Eddy Simulations (LES) of methane point-source emissions to train a Convolutional Neural Network (CNN) on a plume-classification task. We observe a significant distribution shift between LES and AVIRIS-NG plumes, primarily caused by high LES plume enhancements. Through a series of image transforms verified through an adversarial approach using a discriminator network, we minimize the distribution shift between synthetic LES plumes and plumes observed by AVIRIS-NG and GAO. CNNs trained on a mixture of LES and real-world plumes, and tested on flightlines from multiple campaigns exhibit an error reduction compared to previous models. The reduction in false-positive plume detections demonstrates that supplementing the limited training data of real methane plumes with LES provides an avenue to make automatic detection more robust for future airborne and spaceborne missions such as SBG, EMIT, and Carbon Mapper.

Previous work has found that as the surface warms the large-scale tropical circulations weaken, convective anvil cloud fraction decreases, and atmospheric static stability increases. Circulation changes inevitably lead to changes in the humidity and cloud fields which influence the surface energetics. The exchange of mass between the boundary layer and the midtroposphere has also been shown to weaken in global climate models. What has remained less clear is how robust these changes in the circulation are to different representations of convection, clouds, and microphysics in numerical models. We use simulations from the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) to investigate the interaction between overturning circulations, surface temperature, and atmospheric moisture. We analyze the underlying mechanisms of these relationships using a 21-member model ensemble that includes both general circulation models and cloud resolving models. We find a large spread in the change of intensity of the overturning circulation. Both the range of the circulation intensity, and its change with warming can be explained by the range of the mean upward vertical velocity. There is also a consistent decrease in the exchange of mass between the boundary layer and the midtroposphere. However, the magnitude of the decrease varies substantially due to the range of responses in both mean precipitation and mean precipitable water. This work implies that despite well understood thermodynamic constraints, there is still a considerable ability for the cloud fields and the precipitation efficiency to drive a substantial range of tropical convective responses to warming.

Increasing wildfire and declining snowpacks in mountain regions threaten water availability. We combine satellite-based fire detection with snow seasonality classifications to examine fire activity in California’s seasonal and ephemeral snow areas. We find a nearly tenfold increase in fire activity during 2020 and 2021 compared to 2001-2019 as measured by satellite data. Accumulation season snow albedo declined 17-77% in two burned sites as measured by in-situ data relative to un-burned conditions, with greater declines associated with increased soil burn severity. By enhancing snowpack susceptibility to melt, decreased snow albedo drove mid-winter melt during a multi-week midwinter dry spell in 2022. Despite similar meteorological conditions in 2013 and 2022, which we link to persistent high pressure weather regimes, minimal melt occurred in 2013. Post-fire differences are confirmed with satellite measurements. Our findings suggest larger areas of California’s snowpack will be increasingly impacted by the compounding effects of dry spells and wildfire.

Weather and climate forecast predictability relies on Land-Atmosphere (L-A) interactions occurring at different time scales. However, evaluation of L-A coupling parameterizations in current land surface models (LSMs) is challenging since the physical processes are complex, and large-scale observations are scarce and uncommon. Recent advancements in satellite observations, in this light, provide a unique opportunity to evaluate the models’ performances at large spatial scales. Using 5-year soil moisture memory (SMM) from Soil Moisture Active and Passive (SMAP) observations, we evaluate L-A coupling performances in 4 prevailing LSMs with both coupled and offline simulations. Multi-model mean comparison at the global scale shows that current LSMs tend to overestimate SMM that is controlled by water-limited processes and vice versa. Large model spreads in SMM are also observed between individual models. The SMM biases are highly dependent on models’ parameterizations, while showing minor relevance to the models’ soil layer depths or the models’ online/offline simulating schemes. Further analyses of two important terrestrial water cycle-related variables indicate current LSMs may underestimate soil moisture that is directly available for evapotranspiration and global flood risks. Finally, a comparison of two soil moisture thresholds indicates that the soil parameters employed in LSMs play an essential role in producing the model’s biases. The satellite estimation of ET at the water-limited stage and soil hydraulic parameters provides readily available information to constrain LSMs, which are essentially important to improve the models’ L-A coupling simulations, as well as other land surface processes such as terrestrial hydrological cycles.

Stereo methods using GOES-17 and Himawari-8 applied to the Hunga Tonga-Hunga Ha’apai volcanic plume on 15 January 2022 show overshooting tops reaching 50-55 km altitude, a record in the satellite era. Plume height is important to understand dispersal and transport in the stratosphere and climate impacts. Stereo methods, using geostationary satellite pairs, offer the ability to accurately capture the evolution of plume top morphology quasi-continuously over long periods. Manual photogrammetry estimates plume height during the most dynamic early phase of the eruption and a fully automated algorithm retrieves both plume height and advection every 10 minutes during a more frequently sampled and stable phase beginning three hours after the eruption. Stereo heights are confirmed with Global Navigation Satellite System Radio Occultation (GNSS-RO) bending angles, showing that most of the plume was lofted 30–40 km into the atmosphere. Cold bubbles are observed in the stratosphere with brightness temperature of ~173K.

Multiplicity of open source remote sensing date platforms help in bringing various opportunities. Spatio-temporal analysis ofa region can help in analysing changes in regional climate over different constituent land use/land cover (LU/LC). This studyderives a pattern of Land Surface Temperature (LST) over a period of 10 years in 11 smart cities of Uttar Pradesh using opensource data and software programs only. Smart cities namely Agra, Aligarh, Bareilly, Jhansi, Kanpur, Lucknow, Moradabad,Prayagraj, Rampur, Saharanpur and Varanasi are studied for LST in year 2010, 2015 and 2019 by using data from BHUVAN,NRSC and Copernicus Global Land Service: Land Cover (CGLS: LC-100) products. Boundary of the smart cities aredigitized form maps of various local authorities. Land use maps are obtained as Annual Landuse Land Cover 250k scaleproducts for year 2010 & 2015 from BHUVAN, NRSC but CGLS: LC-100 products are of resolution 100 m for year 2019.Both the Land use products are having 12 classes in region of smart cities which are reclassified into 5 LU classes of Built-up, Vegetation, Crop land, Barren land and Water. USGS Earth Explore is used to generate LST for year 2010 throughLandsat-5 ETM images by At-Surface Brightness Temperature & for year 2015 and 2019 through Landsat-8 TIRS bandimages by Radiative Transfer equation. Analysis of LST over years and LU classes show that smart cities of Aligarh andJhansi are dominantly warm over other smart cities of Uttar Pradesh. Capital city of Lucknow and Moradabad smart city arerelatively cooler than other smart cities. Rampur and Jhansi are having the lowest and highest standard deviation in LSTrespectively. Difference in LST over smart cities can be in range of 10-15 °C. Barren Land in these smart cities is found to behotter than Built-up land use class and vegetation is having lowest LST in all 11 smart cities. Range between LST values indifferent years over different LU classes vary between 28-35 °C. In Year 2019 LST statistics seem to be cooled down afteryear 2015 being worst in terms of LST range, maximum value and standard deviation of 6.12 °C. Percentage of vegetationhelping in reducing LST is surely a motivation to apply concept of Urban Green Space (UGS) in these 11 smart cities.

Exposure to fine particulate matter (PM2.5) air pollution is associated with large-scale health consequences, but the uncertainties in estimates of PM2.5-related global premature mortality remain understudied. Using four observation-based PM2.5 datasets and six Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models, we compare uncertainties in current PM2.5-related mortality estimates to the impacts of emissions reductions on global health. Although estimates of current mortality are sensitive to the PM2.5 dataset (6.54 to 8.27 million/year using the Global Exposure Mortality Model), the projected near-term and long-term benefits of emissions reductions for reduced mortality are much more certain. Specifically, uncertainties in projected avoided deaths are consistently less than half the magnitude of uncertainties in recent mortality estimates. Under a low-emissions scenario, avoided cumulative deaths would exceed a quarter-billion by 2100.

One of the most important components of an atmospheric radiation scheme is its treatment of gas optical properties, which determines not only the accuracy of its radiative forcing calculations fundamental to climate prediction, but also its computational cost. This paper describes a free software tool ‘ecCKD’ for generating fast gas-optics models by optimally dividing the spectrum into pseudo-monochromatic spectral intervals (known as k-terms) according to a user-specified error tolerance and the range of greenhouse-gas concentrations that needs to be simulated. The models generated use the correlated k-distribution method in user-specified bands, but can also generate accurate ‘full-spectrum correlated-k’ models that operate on the entire longwave or near-infrared parts of the spectrum. In the near-infrared, the large spectral variation in cloud absorption is represented by partitioning the parts of the spectrum where gases are optically thin into three or more sub-bands, while allowing k-terms for the optically thicker parts of the spectrum (where clouds and surface reflectance are less important) to span the entire near-infrared spectrum. Candidate models using only 16 and 32 k-terms in each of the shortwave and longwave are evaluated against line-by-line calculations on clear and cloudy profiles. The 32-term models are able to accurately capture the radiative forcing of varying greenhouse gases including CO2 concentrations spanning a factor of 12, and heating rates at pressures down to 1 Pa.

Seasonal Ice Zone Reconnaissance Surveys (SIZRS) is a multi-investigator program of repeated ocean, ice, and atmospheric measurements. These measurements make use of U.S. Coast Guard flights across the Beaufort-Chukchi Sea seasonal sea ice zone (SIZ), the region between maximum winter ice extent and minimum summer ice extent. The long-term goal of SIZRS is to track and understand the interplay among the ice, atmosphere, and ocean, contributing to the rapid decline in summer ice extent. The fundamental SIZRS approach is to make monthly flights, June to October, with US Coast Guard Air Station Kodiak C-130s across the Beaufort Sea SIZ along 150°W from 72°N to 76°N or ~ 1 degree of latitude north of the ice edge, whichever is farther north. We make oceanography stations every degree of latitude by dropping Aircraft eXpendable CTDs (AXCTDs) and Aircraft eXpendable Current Profilers (AXCPs) typically while traveling northbound (PI: J. Morison). On the return leg, we drop atmospheric dropsondes from 3000 meters altitude to measure atmospheric temperature, humidity, and winds (PI: A. Schweiger). We also drop UpTempO drifting buoys that report time series of ocean temperature profiles (PI: M. Steele) and various meteorology and ice-tracking buoys of the International Arctic Buoy Program (IABP, PI: I. Rigor).

Preservation of organic carbon (OC) in marine and terrestrial deposits is enhanced by bonding with reactive iron (FeR) phases. The association of OC with FeR (OC-FeR) provides physical protection and hinders microbiological degradation. Roughly 20% of all OC stored in unconsolidated marine sediments and 40% of all OC present in Quaternary terrestrial deposits is preserved as OC-FeR, but this value varies from 10 to 80% across depositional environments. In this work, we provide a new assessment of global OC-FeR burial rates in both marine and terrestrial environments, using published estimates of the fraction of OC associated with FeR, carbon burial, and probabilistic modelling. We estimate the marine OC-FeR sink at between 31 – 70 Mt C yr-1 (mean 52 Mt C yr-1), and the terrestrial OC-FeR sink at between 171 - 946 Mt C yr-1 (mean 472 Mt C yr-1). In marine environments, continental shelves (mean 17 Mt C yr-1) and deltaic/estuarine environments (mean 11 Mg C yr-1) are the primary locations of OC-FeR burial. On land, croplands (279 Mt C yr-1) and grasslands (121 Mt C yr-1) dominate the OC-FeR burial budget. Changes in the Earth system through geological time likely alter the OC-FeR pools, particularly in marine locations. For example, periods of intense explosive volcanism may lead to increased net OC-FeR burial in marine sediments. Our work highlights the importance of OC-FeR in marine carbon burial and demonstrates how OC-FeR burial rates may be an order of magnitude greater in terrestrial environments, those potentially most sensitive to anthropogenic impacts.

The plant hydrodynamic approach represents a recent advancement to land surface modeling, in which stomatal conductance responds to water availability in the xylem rather than in the soil. To provide a realistic representation of tree hydrodynamics, hydrodynamic models must resolve processes at the level of a single modelled tree, and then scale the resulting fluxes to the canopy and land surface. While this tree-to-canopy scaling is trivial in a homogeneous canopy, mixed-species canopies require careful representation of the species properties and a scaling approach that results in a realistic description of both the canopy and individual-tree hydrodynamics, as well as leaf-level fluxes from the canopy and their forcing. Here, we outline advantages and pitfalls of three commonly used approaches for representing mixed-species forests in land surface models, and present a new framework for scaling vegetation characteristics and fluxes in mixed-species forests. The new formulation scales fluxes from the tree- to canopy-level in an energy- and mass-conservative way and allows for a consistent multi-species canopy description for hydrodynamic models.