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.

Rooting depth is an ecosystem trait that determines the extent of soil development and carbon (C) and water cycling. Recent hypotheses propose that human-induced changes to Earth’s biogeochemical cycles propagate deeply due to rooting depth changes from agricultural and climate-induced land cover changes. Yet, the lack of a global-scale quantification of rooting depth responses to human activity limits knowledge of hydrosphere-atmosphere-lithosphere feedbacks in the Anthropocene. Here we use land cover datasets to demonstrate that root depth distributions are changing globally as a consequence of agricultural expansion truncating depths above which 99% of root biomass occurs (D99) by ~60 cm, and woody encroachment linked to anthropogenic climate change extending D99 in other regions by ~38 cm. The net result of these two opposing drivers is a global reduction of D99 by 5%, or ~8 cm, representing a loss of ~11,600 km3 of rooted volume. Projected land cover scenarios in 2100 suggest additional future D99 shallowing of up to 30 cm, generating further losses of rooted volume of ~43,500 km3, values exceeding root losses experienced to date and suggesting that the pace of root shallowing will quicken in the coming century. Losses of Earth’s deepest roots — soil-forming agents — suggest unanticipated changes in fluxes of water, solutes, and C. Two important messages emerge from our analyses: dynamic, human-modified root distributions should be incorporated into earth systems models, and a significant gap in deep root research inhibits accurate projections of future root distributions and their biogeochemical consequences.

The intraseasonal variation of the Tibetan Plateau summer monsoon (TPSM) during 1979–2011 is investigated. The TPSM shows a dominant quasi-biweekly oscillation (QBWO) in most summer seasons, and its active/break phases are closely related to more/less precipitation over the Tibetan Plateau. We suggest that the TPSM QBWO is associated with a southeastward propagating nonstationary wave train in the middle and upper troposphere. It shows equivalent barotropic vertical structures over the midlatitudes and a baroclinic structure over the eastern Tibetan Plateau. Wave activity flux analysis indicates that it originates from northern Europe, which is an active center of the summertime Arctic Oscillation (AO). The AO also shows significant QBWO signals and leads TPSM QBWO by about 13 days. Phase composite and wave activity flux analyses of AO QBWO confirmed that the wave train influences TPSM QBWO, suggesting that AO plays an important role in the TPSM on a 10- to 20-day timescale.

The vertical distribution of plant roots in the soil profile is a key trait modulating plant contributions to soil carbon storage, drought and nutrient stress resistance, yield, and fitness. However, direct sampling of deep roots requires massive effort, so existing data are sparse and many researchers have adopted modeling approaches to fill data gaps and generate hypotheses about how soil properties change the biogeochemical, agricultural, ecological, and hydrologic consequences of root depth. Such models are useful only if they correctly represent the processes of interest and give accurate predictions of the root systems they simulate. Most current root growth models represent soil as a uniform and unrestrictive medium. This is often a reasonable simplification when modeling roots grown in pots or artificial media, but is less so for field soils which often increase in density, hardness, and heterogeneity with depth. To better predict the effect of soil hardness on root distribution, we updated the structural-functional root growth model OpenSimRoot to explicitly predict soil hardness from soil bulk density, water content, porosity, and depth. Root growth impedance is curently represented by linear scaling of the root elongation rate according to soil hardness. Future work will incorporate configurable growth responses and allow hardness to control changes in root diameter and growth direction, thus allowing the model to examine the fitness implications of carbon reallocation in complex structured soils. Our updated OpenSimRoot captured >50% of observed variation in penetrometer resistance from field soils. When we incorporated soil hardness into simulations of maize growth, we observed a substantial reduction in the predicted root:shoot ratio that overwhelmed previous model predictions of increased water uptake from steeper root angles. These findings reinforce that models considering costs and benefits of deep rooting should routinely consider soil hardess.

Rangelands cover over 50% of the land surface area in the western US, providing important economic, social and environmental benefits. The resilience of western rangelands, however, is threatened by climate change, including altered phenology and precipitation patterns, increased frequency and intensity of drought and forest fires, heightened pressure from invasive plants, and reduced water storage in winter snowpack. Climate adaptation strategies are available to ranchers, yet uptake varies substantially. Rancher decision-making is a complex function of their beliefs, knowledge, skill level, risk perceptions, and the institutions supporting them. Semi-structured interviews, focus groups and workshops will be utilized to examine how ranchers in Idaho, Montana, Wyoming perceive and respond to climate change, and the opportunities and barriers these social processes create for climate change adaptation.

As the sea level rises, it is alarming that the threat from flooding induced by tropical cyclones would cause more severe damages to coastal regions worldwide. In order to address this threat, optimizing coastal protective or mitigation strategies is necessary, given limited resources. The optimization methodology must incorporate feedback from stakeholders for practical use. Multiple interviews were conducted by engineering model developers and social scientists with stakeholders who have first-hand knowledge and varied backgrounds in New York. The protective strategies have been tuned to the critical infrastructure's particular and contextual risks due to flood hazards by engaging and integrating stakeholders' knowledge on the interdependency of the infrastructures and other aspects after the first interview. The second interview was conducted for further model improvement.

Some of the Earth system data products such as those from NASA airborne and field investigations (a.k.a. campaigns), are highly heterogeneous and cross-disciplinary, making the data extremely challenging to manage. For example, airborne and field campaign measurements tend to be sporadic over a period of time, with large gaps. Data products generated are of various processing levels and utilized for a wide range of inter- and cross-disciplinary research and applications. Data and derived products have been historically stored in a variety of domain-specific standard (and some non-standard) formats and in various locations such as NASA Distributed Active Archive Centers (DAACs), NASA airborne science facilities, field archives, or even individual scientists’ computer hard drives. As a result, airborne and field campaign data products have often been managed and represented differently, making it onerous for data users to find, access, and utilize campaign data. Some difficulties in discovering and accessing the campaign data originate from the incomplete data product and contextual metadata that may contain details relevant to the campaign (e.g. campaign acronym and instrument deployment locations), but tend to lack other significant information needed to understand conditions surrounding the data. Such details can be burdensome to locate after the conclusion of a campaign. Utilizing consistent terminology, essential for improved discovery and reuse, is also challenging due to the variety of involved disciplines. To help address the aforementioned challenges faced by many repositories and data managers handling airborne and field data, this presentation will describe stewardship practices developed by the Airborne Data Management Group (ADMG) within the Interagency Implementation and Advanced Concepts Team (IMPACT) under the NASA’s Earth Science Data systems (ESDS) Program.

This study investigates trends in global tropical cyclone (TC) activity from 1990–2020, a period where observational platforms are mostly consistent. Several global TC metrics have decreased during this period, with significant decreases in hurricanes and Accumulated Cyclone Energy (ACE). Most of this decrease has been driven by significant downward trends in the western North Pacific. Globally, short-lived named storms, 24-hr intensification periods of >=50 kt day-1 and TC-related damage have increased significantly. The increase in short-lived named storms is likely due to technological improvements, while rapidly intensifying TC increases may be fueled by higher potential intensity. Damage increases are largely due to increased coastal assets. The decreasing trends in hurricane numbers and global ACE are likely due to the trend towards a more La Niña-like base state from 1990–2020, favoring TC activity in the North Atlantic and suppressing TC activity in the eastern and western North Pacific.

Studies of intraseasonal and annual cycles of meteorological variables, using projections of climate change, are nowadays extremely important to improve regional socio-economic planning for countries. This is particularly true in Costa Rica, as Central America has been identified as a climate change hot spot. Today many of the economic activities in the region, especially those related to agriculture, tourism and hydroelectric power generation are linked to the seasonal cycle of precipitation. Changes in rainfall (mm/day) and in the diurnal temperature range ($^{\circ}$C) for the periods 1970-1999 and 2070-2099 were investigated using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) constructed using the CMIP5 (Coupled Model Intercomparison Project version 5) data. Differences between the multi-model ensembles of the two prospective scenarios (RCP 4.5 and RCP 8.5) and the retrospective baseline scenario were computed. This study highlights Costa Rica as an inflexion point of the climate change in the region and also suggests an early onset of the rainy season and future drying conditions.

A supermodel connects different models interactively so that their systematic errors compensate and achieve a model with superior performance. It differs from the standard non-interactive multi-model ensembles (NI), which combines model outputs a-posteriori. We formulate the first supermodel framework for Earth System Models (ESMs) and use data assimilation to synchronise models. The ocean of three ESMs is synchronised every month by assimilating pseudo sea surface temperature (SST) observations generated from them. Discrepancies in grid and resolution are handled by constructing the synthetic pseudo-observations on a common grid. We compare the performance of two supermodel approaches to that of the NI for 1980—2006. In the first (EW), the models are connected to the equal-weight multi-model mean, while in the second (SINGLE), they are connected to a single model. Both versions achieve synchronisation in locations where the ocean drives the climate variability. The time variability of the supermodel multi-model mean SST is reduced compared to the observed variability; most where synchronisation is not achieved and is bounded by NI. The damping is larger in EW than in SINGLE because EW yields additional damping of the variability in the individual models. Hence, under partial synchronisation, the part of variability that is not synchronised gets damped in the multi-model average pseudo-observations, causing a deflation during the assimilation. The SST bias in individual models of EW is reduced compared to that of NI, and so is its multi-model mean in the synchronised regions. The performance of a trained supermodel remains to be tested.

Volcanism is the dominant natural source of mercury (Hg) to the atmosphere, biosphere, ocean and sediments. In recent years, sedimentary Hg contents have emerged as a tool to reconstruct volcanic activity, and particularly activity of (subaerially emplaced) large igneous provinces (LIP) in geological deep time. More specifically, Hg has shown potential as a useful proxy to illuminate the previously elusive impact of such large-scale volcanism on marine and terrestrial paleo-environments. While Hg is now widely applied as volcanism tracer, non-volcanic factors controlling sedimentary Hg content are generally not well constrained. Part of this uncertainty stems from our inability to directly observe a natural unperturbed “steady-state” environment as a baseline, as the modern Hg cycle is heavily influenced by anthropogenic activity. Here we focus on the effects of ambient redox conditions in the water column and shallow sediments (early diagenesis), quantify their influence on the geological Hg record and thereby constrain their potential impact on the use of Hg as a proxy for deep-time volcanic activity. Constraining these factors is of critical importance for the application of Hg as a proxy. Many periods in the geological past for which records have been generated, such as the Mesozoic Oceanic Anoxic Events, are marked by a variety of high-amplitude environmental perturbations, including widespread deoxygenation and deposition of organic-rich sediments. We estimate the impact of redox changes and early diagenesis on the geological Hg record using a suite of (sub)recent–Pleistocene and Upper Cretaceous sediments representing oxic to euxinic marine conditions. Our sample set includes a transect through an oxygen minimum zone and cores that record transient shifts in oxygenation state, as well as post-depositional effects – all unrelated to volcanism, to the best of our knowledge. We find substantial alterations to the Hg record and the records of organic carbon and total sulfur, which are typically assumed to be the most common carrier phases of Hg in marine sediments. Moreover, these biases can lead to signal-alterations on a par with those interpreted to result from volcanic activity. Geochemical modifications are ubiquitous and their potential magnitude implies that the factors leading to biases in the geological record warrant careful consideration before interpretation. Factors of particular concern to proxy application are (1) the disproportionate loss of organic carbon and sulfur compounds relative to Hg during oxidation that strongly modulates normalized Hg records, (2) the evasion of Hg in anoxic and mildly euxinic sediments and (3) sharp focusing of Hg during post-depositional oxidation of organic matter.