Relic coastal landforms (fossil corals, cemented intertidal deposits, or erosive features carved onto rock coasts) serve as sea-level index points (SLIPs) widely used to reconstruct past sea-level changes. Traditional SLIP-based sea-level reconstructions face challenges in capturing continuous sea-level variability and dating erosional outcrops, such as ubiquitous tidal notches, carved around tidal level on carbonate cliffs. We propose a novel approach to such challenges by using a numerical cliff erosion model embedded within a Monte-Carlo simulation to investigate the most likely sea-level scenarios responsible for shaping one of the best-preserved tidal notches of the Last Interglacial age in Sardinia, Italy. Results align with Glacial Isostatic Adjustment model predictions, indicating that synchronized or out-of-sync ice-volume shifts in Antarctic and Greenland ice sheets can reproduce the notch morphology, with sea level confidently peaking at 6m. This new approach yields continuous sea-level insights, bridging gaps in traditional methods and illuminating past Interglacial sea-level dynamics.

There is a need to improve the prediction of tropical cyclone (TC) rainfall as climate change has led to increased TC rainfall rates. Enhanced reliability in predicting TC tracks has paved the way for statistical methodologies to utilize them in estimating current TC rainfall, achieved by identifying similar past TC tracks and obtaining their corresponding rainfall data. The widely used Fuzzy C Means (FCM) clustering algorithm, though popular, has limitations stemming from its clustering-centric design, hindering its ability to pinpoint the most appropriate similar TCs. Our study introduces the Sinkhorn Distance as a novel measure of TC similarity in rainfall prediction. Our findings indicate that the incorporation of Sinkhorn Distance significantly enhances the accuracy of TC rainfall predictions across WNP. When compared to the conventional approach using FCM, our Sinkhorn Distance-based methodology consistently yields better results, as demonstrated by metrics like RMSE and correlation coefficients. Collectively, the inclusion of Sinkhorn Distance stands as a valuable addition to our toolkit for discerning similar TC tracks, thus elevating the precision of TC rainfall predictions. With ongoing advancements in statistical and AI techniques, we anticipate even more refined approaches to further enhance our predictive capabilities. This study represents a leap forward in meeting the critical need for more accurate TC rainfall forecasts in the WNP Region.

Wildfires are increasingly impacting social and environmental systems in the United States. The ability to mitigate the adverse effects of wildfires increases with understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis-Fire Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the United States. FPA FOD v6 contains information on location, jurisdiction, discovery time, cause, and final size of >2.3 million wildfires from 1992-2020 in the United States. For each wildfire, we added physical (e.g., weather, climate, topography, infrastructure), biological (e.g., land cover, normalized difference vegetation index), social (e.g., population density, social vulnerability index), and administrative (e.g., national and regional preparedness level, jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including development of machine learning models.

NOAA’s Daily Optimum Interpolation Sea Surface Temperature (DOISST) indicates that globally averaged sea surface temperature (SST) broke record in March 2023 and set new record highs in April, July, and August 2023. This has raised intense media interest and public concern about causes and connections to climate change. Our analysis indicates that the record high SSTs qualified as marine heatwaves (MHWs) and even super-MHWs as defined in this study, and are attributed to three factors: (i) a linear trend, (ii) a shift to the warm phase of the multi-decadal Pacific-Atlantic-Arctic Oscillation (PAO) pattern which is identified in this study, and (iii) the transition from the triple-dip succession of La Niña events to the 2023 El Niño event. One-Sentence Summary The extreme warm SSTs in 2023 resulted from linear warming trends, a pattern of low-frequency oscillation, and the El Niño event.

A document by Aodhan John Sweeney. Click on the document to view its contents.

Clouds are critical to the biodiversity and function of Tropical Montane Cloud Forests (TMCF). These ecosystems provide vital services to humanity and are considered hotspots of endemism, given that the number of species is restricted to their microclimates. Cloudiness (e.g., the fraction of low-clouds) in these ecosystems is projected to decline owing to global warming, but recent temporal trends remain unclear. Here, we evaluated trends in low-cloud fractions (CF) and other Essential Climatic Variables (ECV) (e.g., surface temperature, pressure, soil moisture, and precipitation) for 521 sites worldwide with TMFCs from 1997 to 2020. Thus, we hypothesize that recent traces of global warming over the last few decades have led to decreases in low-cloud cover on TMCFs. The previous study was also evaluated globally and among biogeographic realms to identify regional trends. We computed trends by aggregating hourly observations from ERA5 reanalysis and CHIRPS into annual averages and then used linear regressions to calculate slopes (i.e., rate of change) (Δ, year-1). Our results suggest that CF trends at the TMCFs range between -64.7 ×10-4 and 51.4 ×10-4 CF year-1, revealing that 70% of the assessed sites have experienced reductions in CF. Declines in low-clouds in these ecosystems are 253% more severe than tropical landmasses when peak values of density distribution are compared (TMCFs: -7.8 ×10-4CF year-1; tropical landmasses -2.3 ×10-4 CF year-1). Despite this, CF trends tend to differ among biogeographic realms, as those TMCFs from the Neotropics and Indomalayan realms have the most pronounced declines. Decreases in CF were also associated with increases in surface temperature and pressure and decreases in soil moisture, revealing that the TMCF’s climate is changing to warmer environments. These climatic shifts may represent a fingerprint of global change on TMCFs, highlighting a current threat to species and essential ecosystem services that these ecosystems provide.

A document by Ron baiman. Click on the document to view its contents.

In this study, we explore the relationship between anvil cloud fraction and horizontal model resolution in small domain radiative-convective equilibrium (RCE) simulations, building on the findings of \citeA{jeevanjee22}. Using the System of Atmosphere Modeling (SAM) model, we find that finer resolutions yield higher anvil cloud fractions due to larger convective updrafts mass flux and increased mass detrainment at anvil levels. Employing two different microphysics schemes, we illustrate that finer resolution can enhance mass flux through either stronger cloud evaporation or weaker upper-troposphere stability, as the consequence of enhanced horizontal mixing. Moreover, we refine an analytical zero-buoyancy plume model to investigate the effects of adjusting entrainment rate and evaporation rate on vertical atmosphere profiles in a simple theoretical framework. Our solutions of the zero-buoyancy plume model suggest that stronger horizontal mixing can lead to larger convective updraft mass flux, consistent with the analysis in numerical simulations. We also observe the likelihood of atmospheric profiles converging at a grid size of approximately 100m, potentially as a result of converging entrainment rate and mixing strength. These insights have implications for global storm-resolving simulations, implying a possible convergence of high cloud and deep convection properties as the horizontal resolution approaches around 100m.

This study assesses less-explored Southern Ocean sea-ice parameters, namely Sea-ice Thickness and Volume, through a comprehensive comparison of 26 CMIP6 models with reanalyses and satellite observations. Findings indicate that models replicate the mean seasonal cycle and spatial patterns of sea-ice thickness, particularly during its maxima in February. However, some models simulate implausible historical mean states compared to satellite observations, leading to large inter-model spread. September sea-ice thickness is consistently biased low across the models. Our results show a positive relationship between modeled mean sea-ice area and thickness in September (i.e., models with more area tend to have thicker ice); in February this relationship becomes negative. While CMIP6 models demonstrate proficiency in simulating Area, thickness accuracy remains a challenge. This study, therefore, highlights the need for improved representation of Antarctic sea-ice processes in models for accurate projections of thickness and volume changes.

The moist processes of the Madden-Julian Oscillation (MJO) in the Coupled Model Intercomparison Project Phase 6 models are assessed using moisture mode theory-based diagnostics over the Indian Ocean (10°S-10°N, 75°E-100°E). Results show that no model can capture all the moisture mode properties relative to the reanalysis. Most models satisfy weak temperature gradient balance but have unrealistically fast MJO propagation and a lower moisture-precipitation correlation. Models that satisfy the most moisture mode criteria reliably simulate the background moist static energy (MSE) and low-level zonal winds compared to models that satisfy the least amount of criteria. The MSE budget associated with the MJO is also well-represented in the good models rather than in the poor models. Our results show that capturing the MJO's moisture mode properties over the Indian Ocean is associated with a more realistic representation of the MJO simulation and thus can be employed to diagnose MJO performance.

The Historical Experiment (HE) data in the Intergovernmental Panel on Climate Change (IPCC) Climate Model Intercomparison Project six (CMIP6) should demonstrate that all submitted models accurately simulate the climate of the recent past. I show: none of nine models analysed accurately creates the known occurrence of ENSO events; no model agrees with any other; and average aerosol levels of the South East Asian Plume (SEAP), the South AMerican Plume (SAMP) and the West African Plume (WAP) are too low. Additionally, the SEAP and the SAMP cause the global temperature to rise in all nine models and the WAP in six models. Hence these models and all others which cannot accurately portray the known occurrence of ENSO events should be withdrawn from CMIP6 until they can and use of the IPCC Assessment Report six (AR6) should be paused until the effects of these aerosol plumes on the global temperature is re-evaluated.

This study explores the carbon stability in the Arctic permafrost following the sea level transgression since the Last Glacial Maximum (LGM). Arctic permafrost is a significant natural reservoir of greenhouse gas which is stored in frozen organic carbon, methane hydrates and natural gas reservoirs. Post-LGM sea level transgression resulted in ocean water, which is up to 20 oC warmer compared to the average annual air mass, inundating, and thawing the permafrost. This study develops a one-dimensional multiphase flow, multicomponent transport numerical model and apply it to investigate the coupled thermal, hydrological, microbial, and chemical processes occurring in the thawing permafrost. Results show that microbial methane is produced and vented to the seawater immediately upon the flooding of the Arctic continental shelves. This microbial methane is generated by biodegradation of the previously frozen organic carbon in the thawing permafrost. The maximum seabed methane flux is predicted in the shallow water where the sediment has been warmed up, but the remaining amount of organic carbon is still high. It is less likely to induce seabed methane emission from methane hydrate dissociation. Such situation only happens when there is very shallow (~200 m depth), intra-permafrost methane hydrate, the occurrence of which is limited. This study provides insights into the limits of methane release from the ongoing flooding of the Arctic permafrost, which is critical to understand the role of the Arctic permafrost in the carbon cycle, ocean chemistry and climate change.

The observed partitioning of poleward heat transport between atmospheric and oceanic heat transports (AHT and OHT) is compared to that in coupled climate models. Poleward OHT in the models is biased low in both hemispheres, with the largest biases in the Southern Hemisphere extratropics. Poleward AHT is biased high in the Northern Hemisphere, especially in the vicinity of the peak AHT near 40$^\circ$N. The significant model biases are persistent across three model generations (CMIP3, CMIP5, CMIP6) and are insensitive to the satellite radiation and atmospheric reanalyses products used to derive observational estimates of AHT and OHT. Model biases in heat transport partitioning are consistent with biases in the spatial structure of energy input to the ocean and atmosphere. Specifically, larger than observed model evaporation in the tropics adds excess energy to the atmosphere that drives enhanced poleward AHT at the expense of weaker OHT

The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal-mean precipitation-minus-evaporation (P-E) under greenhouse-gas forcing. It is shown that cloud feedbacks act to narrow bands of tropical P-E and increase P-E in the deep tropics. The surface-albedo feedback shifts the location of maximum tropical P-E and increases P-E in the polar regions. The intermodel spread in the P-E changes associated with feedbacks arises mainly from cloud feedbacks, with the lapse-rate and surface-albedo feedbacks playing important roles in the polar regions. The P-E change associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the “wet-gets-wetter, dry-gets-drier” paradigm.

A document by Shelley Stall. Click on the document to view its contents.

Ecosystems at high latitudes are under increasing stress from climate change. To understand changes in carbon fluxes, in situ measurements from eddy covariance networks are needed. However, there are large spatiotemporal gaps in the high-latitude eddy covariance network. Here we used the relative extrapolation error index in machine learning-based upscaled gross primary production as a measure of network representativeness and as the basis for a network optimization. We show that the relative extrapolation error index has steadily decreased from 2001 to 2020, suggesting diminishing upscaling errors. In experiments where we limit site activity by either setting a maximum duration or by ending measurements at a fixed time those errors increase significantly, in some cases setting the network status back more than a decade. Our experiments also show that with equal site activity across different theoretical network setups, a more spread out design with shorter-term measurements functions better in terms of larger-scale representativeness than a network with fewer long-term towers. We developed a method to select optimized site additions for a network extension, which blends an objective modeling approach with expert knowledge. Using a case study in the Canadian Arctic we show several optimization scenarios and compare these to a random site selection among reasonable choices. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations.

The global distribution of high Remote-sensing reflectance (Rrs) waters visible from satellite, likely associated with coccolithophore blooms, has changed markedly over the past 40 years. Over that period there has globally been an overall decrease in bloom area of 1.15 million km2 but with notable Rrs increases in the Barents Sea and the Antarctic Ocean. The primary drivers of these fundamental changes to ocean biogeochemistry have been investigated using Machine Learning techniques together with contemporaneous global multi-decadal time-series of sea-surface temperature (SST); wind speed and stress; sea level anomaly (SLA); photosynthetically available radiation (PAR) and; mixed layer depth (MLD). When split into ocean provinces different drivers of positive and negative trends in Rrs were found to dominate in different regions, but generally increases were found to coincide with changes to SST, PAR and reductions to wind-speed.

The Paleocene-Eocene Thermal Maximum (PETM) was a transient global warming event recognised in the geologic record by a prolonged negative carbon isotope excursion (CIE). The onset of the CIE was the result of a rapid influx of 13C-depleted carbon into the ocean-atmosphere system. However, the mechanisms required to sustain the negative CIE remains unclear. Previous studies have identified enhanced mobilisation of petrogenic organic carbon (OCpetro) and argued that this was likely oxidised, increasing atmospheric carbon dioxide (CO2) concentrations after the onset of the CIE. With existing evidence limited to the mid-latitudes and subtropics, we determine whether: (i) enhanced mobilisation and subsequent burial of OCpetro in marine sediments was a global phenomenon; and (ii) whether it occurred throughout the PETM. To achieve this, we utilised a lipid biomarker approach to trace and quantify OCpetro burial in a global compilation of PETM-aged shallow marine sites (n = 7, including five new sites). Our results confirm that OCpetro mass accumulation rates (MARs) increased within the subtropics and mid-latitudes during the PETM, consistent with evidence of higher physical erosion rates and intense episodic rainfall events. The high-latitude sites do not exhibit distinct changes in the organic carbon source during the PETM. This may be due to the more stable hydrological regime and/or additional controls. Crucially, we also demonstrate that OCpetro MARs remained elevated during the recovery phase of the PETM. Although OCpetro oxidation was likely an important positive feedback mechanism throughout the PETM, we show that this feedback was both spatially and temporally variable.

Global Hydrological and Land Surface Models (GHM/LSMs) embody numerous interacting predictors and equations, complicating the diagnosis of primary hydrological relationships. We propose a model diagnostic approach based on Random Forest feature importance to detect the input variables that most influence simulated hydrological processes. We analyzed the JULES, ORCHIDEE, HTESSEL, SURFEX and PCR-GLOBWB models for the relative importance of precipitation, climate, soil, land cover and topographic slope as predictors of simulated average evaporation, runoff, and surface and subsurface runoffs. The machine learning model could reproduce GHM/LSMs outputs with a coefficient of determination over 0.85 in all cases and often considerably better. The GHM/LSMs agreed precipitation, climate and land cover share equal importance for evaporation prediction, and mean precipitation is the most important predictor of runoff. However, the GHM/LSMs disagreed on which features determine surface and subsurface runoff processes, especially with regards to the relative importance of soil texture and topographic slope.

The Yangtze River Valley (YRV) experienced an unprecedented heatwave in midsummer of 2022, but the detailed physical processes involved in the influence of anomalous large-scale atmospheric circulation on the heatwave remain unknown. Here, we show that the positive meridional gradient of anomalous atmospheric moisture at the middle-lower troposphere and associated extreme dry air advection over the YRV are key prerequisites for the formation of the 2022 YRV heatwave. The 2022 YRV heatwave is dominated by the interannual variability, which contributes 72.7% to the total temperature anomalies. Diagnosis of the surface heat budget equation indicates that the surface cloud radiative forcing is the most important process in driving the 2022 YRV heatwave, which is dominated by the positive surface short-wave cloud radiative forcing associated with the suppressed precipitation and the middle-low clouds. The suppressed precipitation is induced by the vertical dynamical processes of anomalous moisture advection caused by the anomalous descending flows over the YRV, which are driven by the negative advection of anomalous latent heat energy by climatological meridional wind (anomalous dry air advection) according to the atmospheric moist static energy equation. Simulations from the Lagrangian model FLEXPART further indicate that the moisture anomaly over the north of YRV is mainly originated from the surface evaporation in the YRV, implying that there is a positive land-air feedback during the life cycle of the YRV heatwave. Our study adds a perspective to the existing mechanism analyses of the 2022 YRV heatwave to serve accurate climate prediction and adaptation planning.

We report an indirect, negative, cloud-mediated, surface radiative effect (RE) of water vapor (IWVE) in certain regions in the tropics, which may be consequential for day-to-day regional heat stress. Using reanalysis and satellite data we show that this effect is marked by a surprisingly dominant positive relationship of cloud RE with near surface and column humidity. These clouds are predominantly low level and altocumuli, previously reported to have a negative surface RE, possibly lending the net negative RE to water vapor. Also reported earlier, these clouds form in the mid-troposphere, as detrainment offshoots of deep convective towers and can be advected away to large distances, hence requiring no local convective triggering in the IWVE regions. Evidently, the IWVE are co-located with the horizontal branch of the Hadley cell, with the lowest vertical forcing in the tropics. Moreover, these are also the transition regions between the highly cloudy and the driest parts of the tropics, with a waning down occurrence of cirrus, deep convective and altostratus clouds, linked with positive RE, corroborating the hypothesis. IWVE regions also show a large temporal variability in humidity possibly providing opportunity for a large variability in cloud fractional coverage, however the mechanism controlling this covariability is not understood. The IWVE is tightly tied with the seasonal cycle of the ITCZ and hence is likely a dominant source of pre-monsoon surface temperature variability and heat stress in the current climate. The evolution of the IWVE under future climate warming needs further investigation.

Recent mass loss from ice sheets and ice shelves is now persistent and prolonged enough that it impacts downstream oceanographic conditions. To demonstrate this, we use an ensemble of coupled GISS-E2.1-G simulations forced with historical estimates of anomalous freshwater, in addition to other climate forcings, from 1990 through 2019. In this ensemble there are detectable differences in zonal-mean sea surface temperatures (SST) and sea ice in the Southern Ocean, and in regional sea level around Antarctica and in the western North Atlantic. These impacts mostly improve the model’s representation of historical changes, including reversing the forced trends in Southern Ocean surface temperature and Antarctic sea ice. The changes in SST may have implications for estimates of the SST pattern effect on climate sensitivity and for cloud feedbacks. We conclude that the changes are sufficiently large that these drivers should be included in all-forcing historical simulations in coupled model intercomparisons.

The Earth System is warming due to anthropogenic greenhouse gas emissions which increases the risk of passing a tipping point in the Earth System, such as a collapse of the Atlantic Meridional Overturning Circulation (AMOC). An AMOC weakening can have large climate impacts which influences the marine and terrestrial carbon cycle and hence atmospheric pCO2. However, the sign and mechanism of this response are subject to uncertainty. Here, we use a state-of-the-art Earth System Model, the Community Earth System Model v2 (CESM2), to study the atmospheric pCO2 response to an AMOC weakening under low (SSP1-2.6) and high (SSP5-8.5) emission scenarios. A freshwater flux anomaly in the North Atlantic strongly weakens the AMOC, and we simulate a weak positive pCO2 response of 0.45 and 1.3 ppm increase per AMOC decrease in Sv for SSP1-2.6 and SSP5-8.5, respectively. For SSP1-2.6 this response is driven by both the oceanic and terrestrial carbon cycles, whereas in SSP5-8.5 it is solely the ocean that drives the response. However, the spatial patterns of both the climate and carbon cycle response are similar in both emission scenarios over the course of the simulation period (2015-2100), showing that the response pattern is not dependent on cumulative CO2 emissions up to 2100. Though the global atmospheric pCO2 response might be small, locally large changes in both the carbon cycle and the climate system occur due to the AMOC weakening, which can have large detrimental effects on ecosystems and society.

The U.S. rice paddy systems play an increasingly vital role in ensuring food security, which also contribute massive anthropogenic non-CO2 (CH4 and N2O) greenhouse gas (GHG) emissions with expanding cultivation area. Yet, the full assessment of GHG balance, considering trade-offs between soil organic carbon (SOC) sequestration and non-CO2 GHG emissions, is lacking. Integrating an improved agricultural ecosystem model with a meta-analysis of multiple field studies, we found that U.S. rice paddy was a rapidly growing net GHG emission source, increased 138% to 8.88 ± 2.65 Tg CO2eq yr-1 in the 2010s. CH4 emission made the most significant contribution (10.12 ± 2.28 Tg CO2eq yr-1) to this increase in net GHG emissions in the 2010s, but increasing N2O emissions, accounting for ~2.4% (0.21 ± 0.03 Tg CO2eq yr-1), cannot be ignored. SOC sequestration could offset about 14.0% (1.45 ± 0.46 Tg CO2eq yr1) of the climate-warming effects of soil non-CO2 GHG emissions in the 2010s. The aggravation of net GHG emissions stemmed from intensified land use/cover changes, rising atmospheric CO2, and heightened synthetic N fertilizer and manure application. Climate change exacerbated around ~21% of soil N2O emissions and ~10% of soil CO2 release in the 2010s. Nonetheless, adopting no/reduced tillage resulted in a substantial decrease of ~10 % in net soil GHG emissions, and non-continuous irrigation exhibited the potential to mitigate around 39% of soil non-CO2 GHG emissions. Great potential for emissions reduction in the mid-South U.S. by optimizing synthetic N fertilizer and manure ratios, reducing tillage, and implementing non-continuous irrigation.