A document by Nicholas Lutsko. Click on the document to view its contents.

Natural climate solutions (NCS) have the potential to achieve up to one-third of emission reductions, but uncertainties surrounding their effectiveness hinder their full realization. Here we employ modern portfolio theory to build NCS portfolios (NCSPs) including a variety of pathways listed in Griscom et al. 1. The different pathways are treated as risky assets within a 'carbon mitigation market' with their returns and risks defined by global estimates of mitigation potential. Our aim is to maximize carbon sequestration while minimizing the risk of carbon loss, thus effectively navigating the 'efficient frontier', where the best trade-off between maximum carbon sequestration and risk occurs. Diversifying pathways leads to decreased risk and enhanced resilience, particularly when risks of carbon loss due to environmental stressors are spatially or temporally uncorrelated. The optimal NCSPs provide valuable insights into distributing investments and land within pathway categories (forests, agriculture and wetlands), intervention types (e.g., manage, protect, restore), cost-effectiveness, and geographical contexts. We hope these results help inform policymakers to reduce risk while pursuing ambitious carbon mitigation targets.

Climate attribution assessments are now common for exceptional weather events, but lesser extremes and everyday weather remain largely unexamined. Here we use a multi-method approach to calculate the influence of human-caused climate change on 54 years of daily temperatures around the world. We use a new metric called the change in information due to perspective that contrasts the likelihood of a temperature in two climates: one forced by extra greenhouse gases and another with no anthropogenic warming. We show that exposure to climate change surged in the middle of 2023. On August 21, a record 4.9 billion people experienced temperatures made at least twice as likely by climate change. On August 22, 49.5% of the land surface reached this level. The distribution of exposure of both ecosystems and countries in August was largely consistent with long-term trends, with higher exposure at night, in tropical ecosystems, and in less developed countries. Notable exceptions to this pattern occurred in Europe and the United States. On August 21, Spain and Italy experienced anomalously warm conditions with very strong climate fingerprints. Over the second half of the year, cities in the United States from Texas to Florida experienced exceptional streaks of extreme and attributable temperatures. We extend the daily attribution approach to quantify how climate change is increasing the exposure of people, especially in Africa and small island states, to stressfully warm temperatures. Daily climate change attribution of temperature provides a new index of climate exposure and new opportunities to communicate about climate change. Significance Statement We quantify the climate fingerprint on local temperature, everywhere and for every day from 1970-2023. We show how the exposure of people and ecosystems to elevated daily temperatures that have been made more likely by climate change surged in 2023. This exposure is not even: it occurs more intensely at night and falls more heavily on countries and ecosystems near the equator. Elevated temperatures are a health risk, and we show that climate change is increasing the exposure of people to stressful daily temperatures. Quantifying climate change on a daily, local scale highlights conditions that will benefit from 1

Understanding the complexities of cloud-sky radiative fluxes is crucial for improving numerical predictions of climate change. NASA's upcoming Atmosphere Observing System (AOS) mission promises unprecedented observations that will present an opportunity to enhance our understanding of the role of clouds in modulating both Earth’s radiation budget and climate sensitivity. AOS will utilize a series of active (Radar, Lidar) and passive (Imaging multi-angle polarimeter) instruments. The active instruments will provide vertically resolved cloud and aerosol information over a narrow ground-track (shown in red in the Figure below), while the passive instruments will cover a much wider swath. The high spatial resolution of AOS (~1km) will allow us to study clouds at the process level. While this will give us the opportunity to gain new insights, it also provides an unprecedented challenge to deliver satellite products at such a high resolution at which horizontal photon transport cannot be neglected, leading to biases in traditional cloud retrieval algorithms. To estimate radiative fluxes over the whole swath, we propose to extrapolate the vertical information from the active instruments to the across-track passive observations using a scene construction algorithm. This algorithm is evaluated using data from Large-Eddy Simulations and synthetic imagery computed by radiative transfer models.

A document by Poulomi Ganguli. Click on the document to view its contents.

A document by Poulomi Ganguli. Click on the document to view its contents.

A document by Poulomi Ganguli. Click on the document to view its contents.

The Antarctic Circumpolar Current is the world's strongest ocean current and plays a disproportionate role in the climate system due to its role as a conduit for major ocean basins. This vast current system is linked to the ocean's vertical overturning circulation, and is thus pivotal to the uptake of heat and CO 2 in the ocean. The strength of the Antarctic Circumpolar Current has varied substantially across warm and cold climates in Earth's past, but the exact dynamical drivers of this change remain elusive. This is in part because ocean models were not able to adequately resolve the eddies and dense shelf water formation that control current strength. Here, we use a global ocean model which resolves such processes to diagnose the impact of future thermal, haline and wind conditions on the strength of the Antarctic Circumpolar Current. By 2050, our model suggests the strength of the Antarctic Circumpolar Current will decline by ∼ 20% in an extreme scenario. This decline is further supported by simple scaling theory, and is driven by ice shelf melting around Antarctica, which weakens the zonal density stratification historically supported by surface temperature gradients. Such a strong decline in transport would have critical implications for the entire global ocean circulation, and hence the Earth's climate system. Southern Ocean | Antarctic Circumpolar Current | Ocean Freshening | Antarctic Bottom Water

Benthic δ18O stacks are the benchmarks by which paleoceanographic data are stratigraphically aligned and compared. However, a recent study found that between 1.8-1.9 million years ago (Ma) several Ceara Rise records differed substantially from the widely used LR04 global stack. Here, we use new Bayesian stacking software to construct regional stacks and demonstrate a geographical divergence in benthic δ18O features from 1.8-1.9 Ma. The pattern of isotopic stage features observed in the Ceara Rise is widespread throughout the Atlantic and differs notably from Pacific records. We propose that this regional difference in isotopic stages may be the result of relatively strong precession forcing and weaker obliquity forcing between 1.8-1.9 Ma. In accordance with the Antiphase Hypothesis, our results highlight a period of apparent sensitivity to regional precession forcing that is masked during most of the 41-kyr world due to the amplitude modulation of obliquity forcing.

www.PosterPresentations.co m The American Southwest is experiencing increased aridity and wildfire incidence, triggering conversion of some frequent-fire forests to non-forest. These dynamics are well-established in ponderosa pine forests, but we know far less about Madrean pine-oak forests in the Sky Islands of Mexico and USA. We have documented scarce pine regeneration and vigorous post-fire oak resprouting in these forests over 27 yrs. We investigated pine regeneration patterns in long-term plots during severe drought, 10 yrs after the Horseshoe 2 Megafire in the Chiricahua Mountains, AZ-a follow-up to a 5-yr assessment. Our goals were to (1) document changes in pine regeneration and (2) develop remote-sensing tools to identify pine refugia across landscapes. For (2), we tested whether two remotely-sensed predictors-Landsat NDVI & ECOSTRESS evapotranspiration-provided predictive power beyond indices of fire severity and topographic moisture. INTRODUCTION The reliability of projections and restoration under intensifying drought and wildfire depends on a fine-grained understanding of refugia for at-risk tree populations. Fire Severity: Landsat differenced Normalized Burn Ratio (dNBR; 30-m resolution): • NBR = (NIR-SWIR) / (NIR + SWIR), dNBR = Pre-fire NBR-Post-fire NBR Topography: • elevation • topo relative moisture index (TRMI) = aspect + position + % slope + surface shape Landsat Normalized Difference Vegetation Index (NDVI; 30-m resolution): • vegetation greenness: NDVI = (NIR-R) / (NIR+ R) ECOSTRESS evapotranspiration (70-m resolution): • land surface temperature + other inputs à Priestly-Taylor algorithm à ET • Conversion of pine-oak forest to oak shrublands continued 6-10 yrs post-fire. Few pine recruits were found in a matrix of dense, oak sprouts, especially after severe fire (FIG 1) • Fewer large pine seedlings in 2021 (a dry season of record aridity) than 2016 • P. leiophylla post-fire resprouts continue to survive and, unlike seedlings, are beginning to overtop the oak resprout canopy (FIG 2) CONCLUSIONS • Nearly three decades of conversion of pine-oak forest to oak shrublands after high-severity wildfire. • Post-fire resprouting, unusual in pines, may be a lifeline for P. leiophylla. • Remotely-sensed Landsat NDVI, combined with topography and fire severity, do a good job of predicting the locations of pine refugia. • ECOSTRESS ET does not help, likely due to larger, less stationary pixels than NDVI • Field data and models suggest P. engelmannii is more drought sensitive and at risk to climate change and wildfires than P. leiophylla. REFERENCES ACKNOWLEDGEMENTS

A document by Tolulope O. Odunola. Click on the document to view its contents.

A document by Mark Hall. Click on the document to view its contents.

Cotton production in the United States has reduced since 1950. The U.S. cotton industry is committed to sustainable cotton production practices that reduce water, land, and energy usage and soil loss while improving soil health and cotton yield. Various climate-smart agriculture strategies have been planned that boost yields and may lower operating costs. Cultivars, soil types, management strategies, pests and diseases, climate, and weather patterns impact crops in complex and nonlinear ways and make crop yield prediction difficult. This is where machine learning (ML) comes in. In this work (Fig.1), we aim to accurately predict cotton yield using an ML method incorporating the effects of climate change, soil variety, cultivars, and the nitrogen from NH4, NO3, and Urea. Two types of cotton yield data—field data and synthetic data—were used. The field data was collected in the 1980s and early 1990s across the southern cotton belt. The dataset consisted of different soil types, cultivars, and amounts of nitrogen. However, these data do not reflect the most recent effects of climate change over the past few years. To address this issue, six years of cotton yield data were generated using the process-based cotton model, GOSSYM. This dataset helps train the ML algorithm with the climate change effect and more precisely predict cotton yield. We concentrated on three southern states: Texas, Mississippi, and Georgia. For each state, three different locations in the cotton-producing counties were chosen. Weather data from 2017 to 2022 for each location were generated using the POWER Data Access Viewer web interface. The same planting and harvest dates were selected for all cases. For each case, the accumulated heat unit (AHU) was calculated from the weather data and used as one of the inputs to the ML model. Instead of applying time series weather data, calculating AHU simplified the scenario, and we were able to reduce the number of computations. Soil types, cultivars, and amounts of nitrogen were varied to create combinations of inputs likely to correspond to the range of farm input factors currently being experienced in these locations. We then developed a Random Forest Regressor to predict the yield. The results show the use of this method is highly accurate (~89%), with R2 averaging around 0.82 and a root mean square error of 117.03 kg/ha.

Cotton is an important cash crop. Its growth and development is influenced by several environmental factors such as change in temperature, amount and distribution of rainfall and carbon dioxide concentration which attribute to climate change. A field experiment was conducted to identify critical meteorological variables of the crop growth stages of the standard weeks over deficit irrigation scheduling on growth, yield and yield components of cotton during 2014 and 2015 kharif season. The experiment was laid out with three standard weeks/ sowing time (24, 26 and 28th) and four deficit irrigation schedules (0.8, 0.6, 0.4 IW/CPE and rain fed) arranged in split plot design. Crop growth parameters, yield, yield components and weather variables were recorded during the study season. The analysis showed those meteorological variables of crop phenophases significant, positive and negative in correlation and regression with growth, yield and yield components of cotton. Among the regressed variables, over 80% impact was noticed for rainfall during square initiation growth stage; and temperature, relative humidity, rainfall, pan evaporation, stress degree day, and intercepted solar radiation during first flower; rainfall, pan evaporation and relative humidity during boll opening growth stage. Thus, it can be concluded that rainfall, maximum temperature, stress degree day, minimum and maximum relative humidity and pan evaporation were found to be significant for cotton growth, yield, and higher quality returns.

Speculations extend the opportunity space of possible future climates by increasing the potential to provide plausible estimated qualities and quantities to further scientific research and aid engineering solutions. This novel work outlines the first steps to achieving an Anthropocene reversal that completes in Zoomers’ lifetimes — by 2100. The novel experimental high-scale carbon removal pathway, which was studied in MAGICC 6.8, required CDR to counterbalance all accumulated anthropogenic emissions since 1750 to return to preindustrial temperature (0.07ºC over the 1720-1800 and 0.14ºC over the 1850-1900) means by 2100 and complete GHG phaseouts by 2077, excluding Ammonia. The experimental pathway set extreme front loading of emissions reductions to reach net zero, and avoid tipping points, then achieve scaled removal to reach 300 ppm of CO2 concentration by roughly mid-century. This work’s findings recommend exploring carbon removal of cumulative anthropogenic emissions totaling 600 GtC to 775 GtC on a recent model ensemble with 1.55 to 1.7 times preindustrial CO2 concentration driven by forcings from emissions and calibrate to reproduce present-day temperatures to provide more detailed projections of temperature, holding below 1.5ºC, regional temperatures, below ground CO2 mineralization, sea-level rise, ENSO, AMOC, and jet-stream turnover, evolve. Continued fossil-fuel use is unable to yield complete emissions phaseouts or deep removals necessary to match a preindustrial climate. The findings support the utmost urgency to attain a maximally scaled sustainable zero-carbon intensity green growth development. And reinforce the increased global commitment to achieve net zero sooner and to avoid setting off more climate tipping points. The possibility of reaching a preindustrial climate should help inform the debate of maximally scaled sustainable green growth development for the fastest path to net zero, phase out of anthropogenic emissions sources, and scaled carbon removals with zero-carbon intensity to develop a more equal future world.

A document by Fiz F. Pérez. Click on the document to view its contents.

A document by Aidin Jabbari. Click on the document to view its contents.

A document by Kennedy Lange. Click on the document to view its contents.

The Paleocene–Eocene Thermal Maximum (PETM) is associated with climatic change and biological turnover. It shares features with the Oceanic Anoxic Events (OAEs) of the Mesozoic, such as transient global warming and biogeochemical perturbations. However, the PETM experienced a more muted expansion of marine anoxia compared to the Mesozoic OAEs (especially OAE 2), with benthic deoxygenation being geographically restricted and limited evidence for photic zone euxinia. We explore the extent and drivers of marine deoxygenation during the PETM using biomarkers for water column euxinia and anoxia and data-constrained biogeochemical climate model (cGENIE) simulations. These reveal that the water column in the North-East Peri-Tethys became anoxic during the PETM, with euxinic conditions reaching the photic zone. Our simulations show that this developed due to a global increase in the ocean nutrient inventory, similar to findings for OAE 2. The particularly strong regional response in the NE Peri-Tethys appears to arise from a combination of global forcing and regionally restricted circulation. Unlike OAE 2, anoxia and PZE do not become widespread in our PETM simulations, consistent with geochemical and biological indicators. This globally muted response could result from a reduced oceanic phosphate inventory prior to the PETM and/or a smaller increase during it relative to the mid-Cretaceous ocean. Our observations suggest that similar feedback mechanisms operated in response to disparate Cenozoic (PETM) and Mesozoic (OAEs) transient global warming events, while also highlighting that background conditions such as geography and nutrient status are crucial in modulating the sensitivity of Earth’s system to them.

This study evaluates the performance of a global storm-resolving model (GSRM), the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM). We analyze marine boundary layer clouds in a cold air outbreak over the Norwegian Sea in a 40-day simulation, and compare them to observations from satellite and a field campaign of the Atmospheric Radiation Measurement program (ARM). SCREAM qualitatively captures the cold air outbreak cloud transition in terms of the boundary layer growth, cloud mesoscale structure, and phase partitioning. SCREAM also correctly locates the greatest ice and liquid in the mesoscale updraft. However, the study finds that SCREAM might underestimate cloud supercooled liquid water in the cumulus cloud regime. This study showcases the promise of employing high-resolution and high-frequency observations under similar large-scale conditions for evaluating GSRMs. This approach can help identify model features for future process-level studies before allocating extra resources for a time-matched model intercomparison of a specific case.

As greenhouse gas emissions increase worldwide, the planet is continuing to warm, changing water amounts and timing. Drought frequency in Canada is expected to increase due to glacier retreat, decreased duration of seasonal snow cover, earlier snow melt, and changing precipitation, along with resulting conditions such as dust storms and wildfires. The predicted increase in drought conditions and resulting exposures to poor air quality demonstrates the importance of researching the impacts of drought conditions on human health, coping methods, and adaptation strategies in the Canadian context due to the relatively few existing studies. This study will look at the wider impacts of drought on the health of Saskatchewan populations as well as coping strategies and adaptation methods of Indigenous groups in Saskatchewan in the face of drought conditions. Studying marginalized communities, such as Indigenous communities who face specific exposures due to their ties to the land, is essential because these communities are likely to experience significant structural barriers and limits to their adaptation given drought impacts. It is important to work with Indigenous communities to understand place-based impacts and culturally appropriate adaptation strategies to inform policy and practice. This project aims to answer the following questions using a coupled human and environment approach of assessing meteorological drivers of drought-induced poor air quality on health: What are the relationships between health impacts and air quality conditions associated with droughts in Saskatchewan over a 12-year period (2010-2022)? and;How have individuals in Indigenous communities in Saskatchewan experienced and been impacted by drought conditions, including coping and adaptation responses implemented by these communities resulting from adverse air quality? The outcomes of this project are to understand the weather conditions that exacerbate air quality as a result of drought to better inform early warning systems and to enhance knowledge, particularly in a Saskatchewan First Nations context, for evidence informed policies, education, and awareness.

Storytelling is a powerful tool for supporting climate literacy. Media can reflect the changes that communities are already making and connect viewers to resources for participating in climate action. This summary provides a touch point for what is currently ongoing in Hollywood as a growing community of practice to include climate mitigation and adaptation information within scripted mainstream media, provide an overview of current challenges, and present suggestions for future work to integrate commercial media into the ecosystem of informal climate education. The new Climate Literacy Guide developed by the U.S. Global Change Research Program can help shape the next decade of storytelling as we build capacity for climate change communication.

The increase in computational power and richness of Earth system data has allowed new methods for simulating natural processes with higher precision and accuracy than previously imagined. Older methods to increase skill of computer model simulations include parameter inference, where the parameters of a forward simulation model are optimized to better represent reality and allow the model to capture dynamics seen in the observed data. However, these methods are limited by our physical understanding of the underlying system, making it impossible to capture certain dynamics when the model is under-represented. Machine learning methods have emerged as a potential tool to bypass the limitations of our physical understanding, and they can create simulations with much higher skill than previous methods. This work investigates and compares the skill of photosynthesis simulations from various model formulations including those with optimized parameters and those from machine learning.

Nutrient limitation is widespread in terrestrial ecosystems. Accordingly, representations of nitrogen (N) limitation in land models typically dampen rates of terrestrial carbon (C) accrual, compared with C-only simulations. These previous findings, however, rely on soil biogeochemical models that implicitly represent microbial activity and physiology. Here we present results from a biogeochemical model testbed that allows us to investigate how an explicit vs. implicit representation of soil microbial activity, as represented in the MIcrobial-MIneral Carbon Stabilization (MIMICS) and Carnegie–Ames–Stanford Approach (CASA) soil biogeochemical models, respectively, influence plant productivity and terrestrial C and N fluxes at initialization and over the historical period. When forced with common boundary conditions, larger soil C pools simulated by the MIMICS model reflect longer inferred soil organic matter (SOM) turnover times than those simulated by CASA. At steady state, terrestrial ecosystems experience greater N limitation when using the MIMICS-CN model, which also increases the inferred SOM turnover time. Over the historical period, however, higher rates of N mineralization were fueled by warming-induced acceleration of SOM decomposition over high latitude ecosystems in the MIMICS-CN simulation reduce this N limitation, resulting in faster rates of vegetation C accrual. Moreover, as SOM stoichiometry is an emergent property of MIMICS-CN, we highlight opportunities to deepen understanding of sources of persistent SOM and explore its potential sensitivity to environmental change. Our findings underscore the need to improve understanding and representation of plant and microbial resource allocation and competition in land models that represent coupled biogeochemical cycles under global change scenarios.