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DOE OSTI2022

Predicting spring green-up across diverse North American grasslands

Vegetation phenology influences many ecosystem and climate processes, such as carbon uptake and energy and water cycles. Thus, understanding drivers of vegetation phenology is crucial for predicting current and future impacts of climate change on ecological systems. Existing models can accurately predict the date of spring green-up in temperate forests but tend to perform poorly in grassland systems. We hypothesize this is because most do not incorporate water availability, a primary limiting factor for grassland plants. In this study, we used long-term datasets of digital imagery from the PhenoCam Network of 43 diverse North American grassland sites (195 site-years) to test existing spring phenology models, as well as develop several new models that incorporate precipitation or soil moisture (53 models). As a result, most of the new models performed substantially better, with the best model requiring sufficient accumulated precipitation followed by warm temperatures to trigger spring onset (root mean square error, RMSE, between predicted and observed dates = 16.0 days). Importantly, the best model performed well across all grassland types using a single set of parameters, from temperate to arid grasslands. Since plants are adapted to their local climates, model performance was further improved when parameters were independently optimized for four separate climate regions (RMSE = 10.4 days). Therefore, both sufficient precipitation and temperature are required for grassland green-up, but optimal thresholds vary by region. Running the top model with projected climate data (representative concentration pathway 8.5) suggests that, depending on the climate region, spring onset will occur up to 12 days earlier within 100 years in temperature-limited sites, but the trend is unclear for precipitation-limited sites (3.5 ± 8.0 days later). This new phenology model improves our ability to understand and predict grassland dynamics, with implications for both current and future ecosystem processes related to carbon and water cycling.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Comparing multi-model ensemble simulations with observations and decadal projections of upper atmospheric variations following the Hunga eruption

The Hunga Tonga–Hunga Ha'apai Model–Observation Comparison (HTHH–MOC) project aims to comprehensively investigate the evolution of volcanic water vapor and sulfur emissions and their subsequent atmospheric impacts and underlying response mechanisms using state-of-the-art global climate models. This study evaluates multi-model ensemble simulations participating in the HTHH–MOC free-run experiment with climate projections for 10 years (2022–2032). Model results are evaluated against satellite observations to assess their ability to reproduce the observed evolution of stratospheric water vapor, aerosols, temperature, and ozone from 2022 to 2024. The participating models accurately capture the observed distribution patterns and associated upper atmospheric responses, providing confidence for their future projections. Model simulations suggest that the Hunga eruption-induced stratospheric water vapor anomaly lasts 4–7 years, with a water vapor e-folding time of 31–43 months. This prolonged water vapor perturbation leads to significant stratospheric and mesospheric cooling, resulting in significant ozone loss in the upper stratosphere and lower mesosphere for 7–10 years. Comparisons between simulations with both SO 2 and H 2 O emissions and those with H 2 O-only emissions indicate that the pronounced dipole response with upper-stratospheric cooling and lower-stratospheric warming is driven by the combined effects of SO 2 and H 2 O injections. These results highlight the prolonged atmospheric impacts of the Hunga eruption and the potential critical role of stratospheric water vapor in modulating long-term atmospheric chemistry and dynamics.

Zhuo, Zhihong [Univ. of Quebec, Montreal, QC (Cana↗

DOE OSTI2023

A convection-permitting dynamically downscaled dataset over the Midwestern United States

Climate change is expected to have far-reaching effects at both the global and regional scale, but local effects are difficult to determine from coarse-resolution climate studies. Dynamical downscaling can provide insight into future climate projections on local scales. Here, we present a new dynamically downscaled dataset for Indiana and the surrounding regions. Output from the Community Earth System Model (CESM) version 1 is downscaled using the Weather Research and Forecasting model (WRF). Simulations are run with a 24-hr reinitialization strategy and a 12-hr spin-up window. WRF output is bias corrected to the National Centers for Environmental Protection/National Center for Atmospheric Research 40-year Reanalysis project (NCEP) using a modified quantile mapping method. Bias-corrected 2-m air temperature and accumulated precipitation are the initial focus, with additional variables planned for future releases. Regional climate change signals agree well with larger global studies, and local fine-scaled features are visible in the resulting dataset, such as urban heat islands, frontal passages, and orographic temperature gradients. This high-resolution climate dataset could be used for down-stream applications focused on impacts across the domain, such as urban planning, energy usage, water resources, agriculture and public health.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model

Abstract Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species–environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold‐adapted species was predicted to have high probabilities of extirpation across its current range—with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

The climate change mitigation potential of annual grasslands under future climates

Abstract Composted manure and green waste amendments have been shown to increase net carbon (C) sequestration in rangeland soils and have been proposed as a means to help lower atmospheric CO 2 concentrations. However, the effect of climate change on soil organic C (SOC) stocks and greenhouse gas emissions in rangelands is not well understood, and the viability of climate change mitigation strategies under future conditions is even less certain. We used a process‐based biogeochemical model (DayCent) at a daily time step to explore the long‐term effects of potential future climate changes on C and greenhouse gas dynamics in annual grassland ecosystems. We then used the model to explore how the same ecosystems might respond to climate change following compost amendments to soils and determined the long‐term viability of net SOC sequestration under changing climates. We simulated net primary productivity (NPP), SOC, and greenhouse gas fluxes across seven California annual grasslands with and without compost amendments. We drove the DayCent simulations with field data and with site‐specific daily climate data from two Earth system models (CanESM2 and HadGEM‐ES) and two representative concentration pathways (RCP4.5 and RCP8.5) through 2100. NPP and SOC stocks in unamended and amended ecosystems were surprisingly insensitive to projected climate changes. A one‐time amendment of compost to rangeland acted as a slow‐release organic fertilizer and increased NPP by up to 390–814 kg C ha −1 year −1 across sites. The amendment effect on NPP was not sensitive to Earth system model or emissions scenario and endured through the end of the century. Net SOC sequestration amounted to 1.96 ± 0.02 Mg C ha −1 relative to unamended soils at the maximum amendment effect. Averaged across sites and scenarios, SOC sequestration peaked 22 ± 1 years after amendment and declined but remained positive throughout the century. Though compost stimulated nitrous oxide (N 2 O) emissions, the cumulative net emissions (in CO 2 equivalents) due to compost were far less than the amount of SOC sequestered. Compost amendments resulted in a net climate benefit of 69.6 ± 0.5 Tg CO 2 e 20 ± 1 years after amendment if applied to similar ecosystems across the state, amounting to 39% of California's rangeland. These results suggest that the biogeochemical benefits of a single amendment of compost to rangelands in California are insensitive to climate change and could contribute to decadal‐scale climate change mitigation goals alongside emissions reductions.

Mayer, Allegra↗

DOE OSTI2021

Photoperiod decelerates the advance of spring phenology of six deciduous tree species under climate warming

Abstract Vegetation phenology in spring has substantially advanced under climate warming, consequently shifting the seasonality of ecosystem process and altering biosphere–atmosphere feedbacks. However, whether and to what extent photoperiod (i.e., daylength) affects the phenological advancement is unclear, leading to large uncertainties in projecting future phenological changes. Here we examined the photoperiod effect on spring phenology at a regional scale using in situ observation of six deciduous tree species from the Pan European Phenological Network during 1980–2016. We disentangled the photoperiod effect from the temperature effect (i.e., forcing and chilling) by utilizing the unique topography of the northern Alps of Europe (i.e., varying daylength but uniform temperature distribution across latitudes) and examining phenological changes across latitudes. We found prominent photoperiod‐induced shifts in spring leaf‐out across latitudes (up to 1.7 days per latitudinal degree). Photoperiod regulates spring phenology by delaying early leaf‐out and advancing late leaf‐out caused by temperature variations. Based on these findings, we proposed two phenological models that consider the photoperiod effect through different mechanisms and compared them with a chilling model. We found that photoperiod regulation would slow down the advance in spring leaf‐out under projected climate warming and thus mitigate the increasing frost risk in spring that deciduous forests will face in the future. Our findings identify photoperiod as a critical but understudied factor influencing spring phenology, suggesting that the responses of terrestrial ecosystem processes to climate warming are likely to be overestimated without adequately considering the photoperiod effect.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2021

Photoperiod Decelerates the Advance of Spring Phenology of Six Deciduous Tree Species under Climate Warming

Vegetation phenology in spring has substantially advanced under climate warming, consequently shifting the seasonality of ecosystem process and altering biosphere-atmosphere feedbacks. However, whether and to what extent photoperiod (i.e., daylength) affects the phenological advancement is unclear, leading to large uncertainties in projecting future phenological changes. Here we examined the photoperiod effect on spring phenology at a regional scale using in situ observation of six deciduous tree species from the Pan European Phenological Network during 1980-2016. We disentangled the photoperiod effect from the temperature effect (i.e., forcing and chilling) by utilizing the unique topography of the northern Alps of Europe (i.e., varying daylength but uniform temperature distribution across latitudes) and examining phenological changes across latitudes. We found prominent photoperiod-induced shifts in spring leaf-out across latitudes (up to 1.7 daysper latitudinal degree). Photoperiod regulates spring phenology by delaying early leaf-out and advancing late leaf-out caused by temperature variations. Based on these findings, we proposed two phenological models that consider the photoperiod effect through different mechanisms and compared them with a chilling model. We found that photoperiod regulation would slow down the advance in spring leaf-out under projected climate warming and thus mitigate the increasing frost risk in spring that deciduous forests will face in the future. Our findings identify photoperiod as a critical but understudied factor influencing spring phenology, suggesting that the responses of terrestrial ecosystem processes to climate warming are likely to be overestimated without adequately considering the photoperiod effect.

Lin Meng↗

NASA NTRS2015

New York City Panel on Climate Change 2015 Report: Climate Observations and Projections - Chapter 1

During 2013 and 2014, numerous international (IPCC, 2013) and national (Melillo et al., 2014; Gordon, 2014) reports have concluded that human activities are changing the climate, leading to increased vulnerability and risk. Since the industrial revolution, fossil fuel burning, industrial activity, and land use changes have led to a 40% increase in heat-trapping carbon dioxide (CO2), and an approximately 150% increase in methane (CH4), another powerful greenhouse gas (GHG), has been observed. Global temperatures have increased by close to 1 C since 1880 as the upper oceans have warmed and polar ice has retreated. These and other climate changes are projected to accelerate as greenhouse gas concentrations continue to rise. In the coming decades, climate change is extremely likely to bring warmer temperatures in the New York metropolitan region (see Box. 1.1 and Fig.1.1 for key definitions and terms). Heat waves are very likely to increase; total annual precipitation will likely increase and brief, intense rainstorms are very likely to increase. Because of incomplete knowledge about exactly how much climate change will occur, choosing among policies for reducing future damages requires prudent risk management (Yohe and Leichenko, 2010; Kunreuther et al., 2013). Given differing risk tolerances among stakeholders, a risk management approach allows for a range of possible climate change outcomes to be examined with associated uncertainties surrounding their likelihoods. The New York City Panel on Climate Change 2 (NPCC2) projections can be used to inform planning across multiple governmental scales (e.g., city, county, state) in the New York metropolitan region. Such coordinated efforts can serve as test cases for successful local, state, and federal coordination for integrated climate adaptation initiatives. This chapter describes the global climate system, and presents observed temperature and precipitation trends and projections for the region. Chapter 2 (NPCC, 2015) focuses on sea level rise and possible changes in coastal storms. Chapter 3 and Chapter 4 (NPCC, 2015) describe efforts to better understand the region's vulnerability to coastal flooding during coastal storms. The treatment of likelihood related to the NPCC projections is similar to that developed by the Intergovernmental Panel on Climate Change Fourth and Fifth Assessment Reports (IPCC, 2007; 2013), with six likelihood categories (Box 1.1 and Fig. 1.1). The assignment of climate hazards to these categories is based on observed data, global climate model simulations, published literature, and expert judgment.

climate change↗

DOE OSTI2021

Differential Credibility of Climate Modes in CMIP6

This work quantitatively evaluates the fidelity with which the northern annular mode (NAM), southern annular mode (SAM), Pacific–North American pattern (PNA), El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Atlantic multidecadal oscillation (AMO), and the first-order mode interactions are represented in Earth system model (ESM) output from the CMIP6 archive. Several skill metrics are used as part of a differential credibility assessment (DCA) of both spatial and temporal characteristics of the modes across ESMs, ESM families, and specific ESM realizations relative to ERA5. The spatial patterns and probability distributions are generally well represented but skill scores that measure the degree to which the frequencies of maximum variance are captured are consistently lower for most ESMs and climate modes. Substantial variability in skill scores manifests across realizations from individual ESMs for the PNA and oceanic modes. Further, the ESMs consistently overestimate the strength of the NAM–PNA first-order interaction and underestimate the NAM–AMO connection. Furthermore, these results suggest that the choice of ESM and ESM realizations will continue to play a critical role in determining climate projections at the global and regional scale at least in the near term.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

The Worldwide C3S CORDEX Grand Ensemble: A Major Contribution to Assess Regional Climate Change in the IPCC AR6 Atlas

The collaboration between the Coordinated Regional Climate Downscaling Experiment (CORDEX) and the Earth System Grid Federation (ESGF) provides open access to an unprecedented ensemble of regional climate model (RCM) simulations, across the 14 CORDEX continental-scale domains, with global coverage. These simulations have been used as a new line of evidence to assess regional climate projections in the latest contribution of the Working Group I (WGI) to the IPCC Sixth Assessment Report (AR6), particularly in the regional chapters and the Atlas. Here, we present the work done in the framework of the Copernicus Climate Change Service (C3S) to assemble a consistent worldwide CORDEX grand ensemble, aligned with the deadlines and activities of IPCC AR6. This work addressed the uneven and heterogeneous availability of CORDEX ESGF data by supporting publication in CORDEX domains with few archived simulations and performing quality control. It also addressed the lack of comprehensive documentation by compiling information from all contributing regional models, allowing for an informed use of data. In addition to presenting the worldwide CORDEX dataset, we assess here its consistency for precipitation and temperature by comparing climate change signals in regions with overlapping CORDEX domains, obtaining overall coincident regional climate change signals. The C3S CORDEX dataset has been used for the assessment of regional climate change in the IPCC AR6 (and for the interactive Atlas) and is available through the Copernicus Climate Data Store (CDS).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Multi-century dynamics of the climate and carbon cycle under both high and net negative emissions scenarios

Abstract. Future climate projections from Earth system models (ESMs) typically focus on the timescale of this century. We use a set of five ESMs and one Earth system model of intermediate complexity (EMIC) to explore the dynamics of the Earth's climate and carbon cycles under contrasting emissions trajectories beyond this century to the year 2300. The trajectories include a very-high-emissions, unmitigated fossil-fuel-driven scenario, as well as a mitigation scenario that diverges from the first scenario after 2040 and features an “overshoot”, followed by a decrease in atmospheric CO2 concentrations by means of large net negative CO2 emissions. In both scenarios and for all models considered here, the terrestrial system switches from being a net sink to either a neutral state or a net source of carbon, though for different reasons and centered in different geographic regions, depending on both the model and the scenario. The ocean carbon system remains a sink, albeit weakened by carbon cycle feedbacks, in all models under the high-emissions scenario and switches from sink to source in the overshoot scenario. The global mean temperature anomaly is generally proportional to cumulative carbon emissions, with a deviation from proportionality in the overshoot scenario that is governed by the zero emissions commitment. Additionally, 23rd century warming continues after the cessation of carbon emissions in several models in the high-emissions scenario and in one model in the overshoot scenario. While ocean carbon cycle responses qualitatively agree in both globally integrated and zonal mean dynamics in both scenarios, the land models qualitatively disagree in zonal mean dynamics, in the relative roles of vegetation and soil in driving C fluxes, in the response of the sink to CO2, and in the timing of the sink–source transition, particularly in the high-emissions scenario. The lack of agreement among land models on the mechanisms and geographic patterns of carbon cycle feedbacks, alongside the potential for lagged physical climate dynamics to cause warming long after CO2 concentrations have stabilized, points to the possibility of surprises in the climate system beyond the 21st century time horizon, even under relatively mitigated global warming scenarios, which should be taken into consideration when setting global climate policy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Datasets for Widespread Residential Space Heating Electrification in Texas

In this experiment, we explore long term patterns in electricity demand driven by the dual effects of full electrification of space heating in Texas (by adoption of electric heat pumps), and climate change. We use a predictive model of electricity demand, climate projections, and an open source nodal power system (DC Optimal Power Flow) model of the Electric Reliability Council of Texas (ERCOT) system. Heat pumps are a more energy efficient way of providing space heating and cooling in homes. We attempt to exhaustively investigate the impacts of full residential space heating electrification by adoption of heat pumps for the segment of Texas households that currently rely on fossil fuels (about 40%), while simultaneously incorporating climate change meteorological variables. We explore a range of scenarios of heat pump efficiency and climate uncertainty over a long period of future years (2020-2099). In total, the simulation experiment generates 1,280 simulation years of hourly data. We report and analyze results in form of impacts on residential load, total load, peak load, seasonality of peaking, and reliability measured by occurrence and frequency of loss of load events. While the experiment is for ERCOT, the insights and approach can be applied to other regions. The results from the analysis can inform system planners on a range of potential capacity requirements/ reliability implications and/or risks of full space heating electrification via the adoption of electric heat pumps, given the uncertainty in the scenarios/ climate futures. The dataset includes model output for residential, non residential and total load, and the results from the GO ERCOT model runs for 4 RCP Scenarios (RCP 4.5 Cooler, RCP 4.5 Hotter, RCP 8.5 Cooler, RCP 8.5 Hotter), 4 Heating electrification Scenarios (Base , Standard Efficiency HP, High Efficiency HP, Ultra-High Efficiency HP) over 80 years (2020-2099). The metrological variables at BA scale were weighted weighted using population projections consistent with the SSP3 scenario.

Climate Change↗

DOE OSTI2023

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore’s Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to MLexperts because of lack of training data and relevant, easy-to-use workflows. Wepresent ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator’s macro-scale physical state. The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res2) and code(https://leap-stc.github.io/ClimSim)arereleasedopenlytosupport the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.

artificial intelligence, machine learning↗

DOE OSTI2025

Assessing the impact of climate change on rainfall-triggered landslides: a case study in California

Landslides are widespread natural hazards which take a heavy toll on lives, property, and infrastructure each year. In January 2023 and 2024, widespread landslides occurred in California as a result of extreme winter precipitation. Estimating the regional evolution of landslide hazard in a changing climate is essential for adaptation planning and risk mitigation efforts. The complex effects of climate change on landslide hazard, however, are poorly understood. Here, we use climate and landslide observations to develop a novel data-driven approach for landslide susceptibility assessment under historical and future climate conditions. We apply this framework to investigate the evolution of landslide susceptibility in California and its impacts on the roadway system based on downscaled climate projections under moderate and high greenhouse gas emission scenarios. On average, the results indicate an increase in landslide susceptibility through 2100 in regions where historical susceptibility is moderate to very high. Although the spatio-temporal variations in landslide susceptibility are sensitive to climate model uncertainties in predicting extreme rainfall, the direst impacts are largely circumvented in lower emission scenarios.

Climate Change↗

DOE OSTI2022

Insights on sources and formation mechanisms of liquid-bearing clouds over MOSAiC examined from a Lagrangian framework

Understanding Arctic stratiform liquid-bearing cloud life cycles and properly representing these life cycles in models is crucial for evaluations of cloud feedbacks as well as the faithfulness of climate projections for this rapidly warming region. Examination of cloud life cycles typically requires analyses of cloud evolution and origins on short time scales, on the order of hours to several days. Measurements from the recent Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition provide a unique view of the current state of the central Arctic over an annual cycle. Here, we use the MOSAiC radiosonde measurements to detect liquid-bearing cloud layers over full atmospheric columns and to examine the cloud-generating air masses’ properties. We perform 5-day (120 h) back-trajectory calculations for every detected cloud and cluster them using a unique set of variables extracted from these trajectories informed by ERA5 reanalysis data. This clustering method enables us to separate between the air mass source regions such as ice-covered Arctic and midlatitude open water. We find that moisture intrusions into the central Arctic typically result in multilayer liquid-bearing cloud structures and that more than half of multilayer profiles include overlying liquid-bearing clouds originating in different types of air masses. Finally, we conclude that Arctic cloud formation via prolonged radiative cooling of elevated stable subsaturated air masses circulating over the Arctic can occur frequently (up to 20% of detected clouds in the sounding data set) and may lead to a significant impact of ensuing clouds on the surface energy budget, including net surface warming in some cases.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2018

Pliocene and Eocene Provide Best Analogs for Near-Future Climates

As the world warms due to rising greenhouse gas concentrations, the Earth system moves toward climate states without societal precedent, challenging adaptation. Past Earth system states offer possible model systems for the warming world of the coming decades. These include the climate states of the Early Eocene (ca. 50 Ma), the Mid-Pliocene (3.3–3.0 Ma), the Last Interglacial (129–116 ka), the Mid-Holocene (6 ka), preindustrial (ca. 1850 CE), and the 20th century. Here, we quantitatively assess the similarity of future projected climate states to these six geohistorical benchmarks using simulations from the Hadley Centre Coupled Model Version 3 (HadCM3), the Goddard Institute for Space Studies Model E2-R (GISS), and the Community Climate System Model, Versions 3 and 4 (CCSM) Earth system models. Under the Representative Concentration Pathway 8.5 (RCP8.5) emission scenario, by 2030 CE, future climates most closely resemble Mid-Pliocene climates, and by 2150 CE, they most closely resemble Eocene climates. Under RCP4.5, climate stabilizes at Pliocene-like conditions by 2040 CE. Pliocene-like and Eocene-like climates emerge first in continental interiors and then expand outward. Geologically novel climates are uncommon in RCP4.5 (<1%) but reach 8.7% of the globe under RCP8.5, characterized by high temperatures and precipitation. Hence, RCP4.5 is roughly equivalent to stabilizing at Pliocene-like climates, while unmitigated emission trajectories, such as RCP8.5, are similar to reversing millions of years of long-term cooling on the scale of a few human generations. Both the emergence of geologically novel climates and the rapid reversion to Eocene-like climates may be outside the range of evolutionary adaptive capacity.

climate change↗

NASA NTRS2018

Identifying and Forecasting Potential Biophysical Risk Areas within a Tropical Mangrove Ecosystem Using Multi-Sensor Data

Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities.

Shanti Shrestha↗

DOE OSTI2022

Diffuse Radiation Forcing Constraints on Gross Primary Productivity and Global Terrestrial Evapotranspiration

Abstract The diffuse radiation fertilization effect—the increase in plant productivity in the presence of higher diffuse radiation ( K ↓,d )—is an important yet understudied aspect of atmosphere‐biosphere interactions and can modify the terrestrial carbon, energy, and water budgets. The K ↓,d fertilization effect links the carbon cycle with clouds and aerosols, all of which are large sources of uncertainties for our current understanding of the Earth system and for future climate projections. Here we establish to what extent observational and modeling uncertainty in sunlight's diffuse fraction ( k d ) affects simulated gross primary productivity (GPP) and terrestrial evapotranspiration ( λE ). We find only 48 eddy covariance sites with simultaneous sufficient measurements of K ↓,d with none in the tropical climate zone, making it difficult to constrain this mechanism globally using observations. Using a land modeling framework based on the latest version of the Community Land Model, we find that global GPP ranges from 114 Pg C year −1 when using k d forcing from the Modern‐Era Retrospective analysis for Research and Applications, version 2 reanalysis to a ∼7% higher value of 122 Pg C year −1 when using the Clouds and the Earth's Radiant Energy System satellite product, with especially strong differences apparent over the tropical region (mean increase ∼9%). The differences in λE , although smaller (−0.4%) due to competing changes in shaded and sunlit leaf transpiration, can be greater than regional impacts of individual forcing agents like aerosols. Our results demonstrate the importance of comprehensively and systematically validating the simulated k d by atmosphere modules as well as the response to differences in k d within land modules across Earth System Models.

54 ENVIRONMENTAL SCIENCES↗