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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Effects of Surface Turbulence Flux Parameterizations on the MJO: The Role of Ocean Surface Waves

This study investigates the sensitivity of the Madden–Julian oscillation (MJO) to changes to the bulk flux parameterization and the role of ocean surface waves in air–sea coupling using a fully coupled ocean–atmosphere–wave model. The atmospheric and ocean model components of the Energy Exascale Earth System Model (E3SM) are coupled to a spectral wave model, WAVEWATCH III (WW3). Two experiments with wind speed–dependent bulk algorithms (NCAR and COARE3.0a) and one experiment with wave-state-dependent flux (COR3.0a-WAV) were conducted. We modify COARE3.0a to include surface roughness calculated within WW3 and also account for the buffering effect of waves on the relative difference between air-side and ocean-side momentum flux. Differences in surface fluxes, primarily caused by discrepancies in drag coefficients, result in significant differences in MJO’s properties. While COARE3.0a has better convection–circulation coupling than NCAR, it exhibits anomalous MJO convection east of the date line. The wave-state-dependent flux (COR3.0-WAV) improves the MJO representation over the default COARE3.0 algorithm. Strong easterlies over the Pacific Ocean in COARE3.0a enhance the latent heat flux (LHFLX). This is responsible for the anomalous MJO propagation after the date line. In COR3.0a-WAV, waves reduce the anomalous easterlies, leading to a decrease in LHFLX and MJO dissipation after the date line. These findings highlight the role of surface fluxes in MJO simulation fidelity. Most importantly, we show that the proper treatment of wave-induced effects in bulk flux parameterization improves the simulation of coupled climate variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Investigating the impact of aerosol deposition on snowmelt over the Greenland Ice Sheet using a large-ensemble kernel

Accelerating surface melt on the Greenland Ice Sheet (GrIS) has led to a doubling of Greenland's contribution to global sea level rise during recent decades. Black carbon (BC), dust, and other light-absorbing impurities (LAIs) darken the surface and enhance snowmelt by boosting the absorption of solar energy. It is therefore important for coupled aerosol–climate and ice sheet models to include snow darkening effects from LAI, and yet most do not. In this study, we conduct several thousand simulations with the Community Land Model (CLM) component of the Community Earth System Model (CESM) to characterize changes in melt runoff due to variations in the amount, timing, and nature (wet or dry) of BC deposition on the GrIS. From this large matrix of simulations, we develop a kernel relating runoff to the location, month, year (from 2006 to 2015), and magnitudes of BC concentration within precipitation and dry deposition flux. BC deposition during June–August causes the largest increase in annually integrated runoff, but winter deposition events also exert large (roughly half as great) runoff perturbations due to reexposure of impurities at the snow surface during summer melt. Current BC deposition fluxes simulated with the atmosphere component of CESM induce a climatological-mean increase in GrIS-wide runoff of ∼ 8 Gt yr −1 , or +6.8 % relative to a paired simulation without BC deposition. We also provide linear equations that relate the increase in total runoff to GrIS-wide wet and dry BC deposition fluxes. It is our hope that the runoff kernel and simple equations provided here can be used to extend the utility of state-of-the-art aerosol models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems

Abstract. Over the past 4 decades, a number of numerical models have been developed to quantify the magnitude, investigate the spatial and temporal variations, and understand the underlying mechanisms and environmental controls of methane (CH4) fluxes within terrestrial ecosystems. These CH4 models are also used for integrating multi-scale CH4 data, such as laboratory-based incubation and molecular analysis, field observational experiments, remote sensing, and aircraft-based measurements across a variety of terrestrial ecosystems. Here we summarize 40 terrestrial CH4 models to characterize their strengths and weaknesses and to suggest a roadmap for future model improvement and application. Our key findings are that (1) the focus of CH4 models has shifted from theoretical to site- and regional-level applications over the past 4 decades, (2) large discrepancies exist among models in terms of representing CH4 processes and their environmental controls, and (3) significant data–model and model–model mismatches are partially attributed to different representations of landscape characterization and inundation dynamics. Three areas for future improvements and applications of terrestrial CH4 models are that (1) CH4 models should more explicitly represent the mechanisms underlying land–atmosphere CH4 exchange, with an emphasis on improving and validating individual CH4 processes over depth and horizontal space, (2) models should be developed that are capable of simulating CH4 emissions across highly heterogeneous spatial and temporal scales, particularly hot moments and hotspots, and (3) efforts should be invested to develop model benchmarking frameworks that can easily be used for model improvement, evaluation, and integration with data from molecular to global scales. These improvements in CH4 models would be beneficial for the Earth system models and further simulation of climate–carbon cycle feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A multi-year short-range hindcast experiment with CESM1 for evaluating climate model moist processes from diurnal to interannual timescales

Abstract. We present a multi-year short-range hindcast experiment and its experimental design for better evaluation of both the mean state and variability of atmospheric moist processes in climate models from diurnal to interannual timescales and facilitate model development. We used the Community Earth System Model version 1 as the base model and performed a suite of 3 d hindcasts initialized every day starting at 00:00 Z from 1997 to 2012. Three processes – the diurnal cycle of clouds during different cloud regimes over the central US, precipitation and diabatic heating associated with the Madden–Julian Oscillation (MJO), and the response of precipitation, surface radiative and heat fluxes, as well as zonal wind stress to sea surface temperature anomalies associated with the El Niño–Southern Oscillation – are evaluated as examples to demonstrate how one can better utilize simulations from this experiment to gain insights into model errors and their connection to physical parameterizations or large-scale state. This is achieved by comparing the hindcasts with corresponding long-term observations for periods based on different phenomena. These analyses can only be done through this multi-year hindcast approach to establish robust statistics of the processes under well-controlled large-scale environment because these phenomena are either a result of interannual climate variability or only happen a few times in a given year (e.g., MJO, or cloud regime types). Furthermore, comparison of hindcasts to the typical simulations in climate mode with the same model allows one to infer what portion of a model's climate error directly comes from fast errors in the parameterizations of moist processes. As demonstrated here, model biases in the mean state and variability associated with parameterized moist processes usually develop within a few days and manifest within weeks to affect the simulations of large-scale circulation and ultimately the climate mean state and variability. Therefore, model developers can achieve additional useful understanding of the underlying problems in model physics by conducting a multi-year hindcast experiment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)

Tropical forest dynamics play a crucial role in the global carbon, water, and energy cycles. However, realistically simulating the dynamics of competition and coexistence between different plant functional types (PFTs) in tropical forests remains a significant challenge. This study aims to improve the modeling of PFT coexistence in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a vegetation demography model implemented in the Energy Exascale Earth System Model (E3SM) land model (ELM), ELM-FATES. Specifically, we explore (1) whether plant trait relationships established from field measurements can constrain ELM-FATES simulations and (2) whether machine learning (ML)-based surrogate models can emulate the complex ELM-FATES model and optimize parameter selections to improve PFT coexistence modeling. We conducted three ensembles of ELM-FATES experiments at a tropical forest site near Manaus, Brazil. By comparing the ensemble experiments without (Exp-CTR) and with (Exp-OBS) consideration of observed trait relationships, we found that accounting for these relationships slightly improves the simulations of water, energy, and carbon variables when compared to observations but degrades the simulation of PFT coexistence. Using ML-based surrogate models trained on Exp-CTR, we optimized the trait parameters in ELM-FATES and conducted another ensemble of experiments (Exp-ML) with these optimized parameters. The proportion of PFT coexistence experiments significantly increased from 21 % in Exp-CTR to 73 % in Exp-ML. After filtering the experiments that allow for PFT coexistence to agree with observations (within 15 % tolerance), 33 % of the Exp-ML experiments were retained, which is a significant improvement compared to the 1.4 % in Exp-CTR. Exp-ML also accurately reproduces the annual means and seasonal variations in water, energy, and carbon fluxes and the field inventory of aboveground biomass. This study represents a reproducible method that utilizes machine learning to identify parameter values that improve model fidelity against observations and PFT coexistence in vegetation demography models for diverse ecosystems. Our study also suggests the need for new mechanisms to enhance the robust simulation of coexisting plants in ELM-FATES and has significant implications for modeling the response and feedbacks of ecosystem dynamics to climate change.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0: Stratospheric Composition

The NASA Goddard Earth Observing System (GEOS) Composition Forecast (GEOS-29CF) provides recent estimates and five-day forecasts of atmospheric composition to the public in near-real time. To do this, the GEOS Earth system model is coupled with the GEOS-Chem tropospheric-stratospheric unified chemistry extension (UCX) to represent composition from the surface to the top of the GEOS atmosphere (0.01 hPa). The GEOS-CF system is described, including updates made to the GEOS-Chem UCX mechanism within GEOS-CF for improved representation of stratospheric chemistry. Comparisons are made against balloon, lidar and satellite observations for stratospheric composition, including measurements of ozone (O3) and important nitrogen and chlorine species related to stratospheric O3recovery. The GEOS-CF nudges the stratospheric O3towards the GEOS Forward Processing (GEOS FP) assimilated O3product; as a result the stratospheric O3in the GEOS-CF historical estimate agrees well with observations. During abnormal dynamical and chemical environments such as the 2020 polar vortexes, the GEOS-CF O3forecasts are more realistic than GEOS FP O3forecasts because of the inclusion of the complex GEOS-Chem UCX stratospheric chemistry. Overall, the spatial patterns of the GEOS-CF simulated concentrations of stratospheric composition agree well with satellite observations. However, there are notable biases – such as low NOx and HNO3 in the polar regions and generally low HCl throughout the stratosphere – and future improvements to the chemistry mechanism and emissions are discussed. GEOS-CF is a new tool for the research community and instrument teams observing trace gases in the stratosphere and troposphere, providing near-real-time three-dimensional gridded information on atmospheric composition.

GEOS-CF↗
NASA NTRS2022

Changes in Anthropogenic Precursor Emissions Drive Shifts in the Ozone Seasonal Cycle Throughout the Northern Midlatitude Troposphere

Simulations by six Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system models indicate that the seasonal cycle of baseline tropospheric ozone at northern midlatitudes has been shifting since the mid-20th century. Beginning in ∼ 1940, the magnitude of the seasonal cycle increased by ∼10 ppb (measured from seasonal minimum to maximum), and the seasonal maximum shifted to later in the year by about 3 weeks. This shift maximized in the mid-1980s, followed by a reversal – the seasonal cycle decreased in amplitude and the maximum shifted back to earlier in the year. Similar changes are seen in measurements collected from the 1970s to the present. The timing of the seasonal cycle changes is generally concurrent with the rise and fall of anthropogenic emissions that followed industrialization and the subsequent implementation of air quality emission controls. A quantitative comparison of the temporal changes in the ozone seasonal cycle at sites in both Europe and North America with the temporal changes in ozone precursor emissions across the northern midlatitudes found a high degree of similarity between these two temporal patterns. We hypothesize that changing precursor emissions are responsible for the shift in the ozone seasonal cycle; this is supported by the absence of such seasonal shifts in southern midlatitudes where anthropogenic emissions are much smaller. We also suggest a mechanism by which changing emissions drive the changing seasonal cycle: increasing emissions of NOx allow summertime photochemical production of ozone to become more important than ozone transported from the stratosphere, and increasing volatile organic compounds (VOCs) lead to progressively greater photochemical ozone production in the summer months, thereby increasing the amplitude of the seasonal ozone cycle. Decreasing emissions of both precursor classes then reverse these changes. The quantitative parameter values that characterize the seasonal shifts provide useful benchmarks for evaluating model simulations, both against observations and between models.

Ozone↗
DOE OSTI2024

Intensification of future subsurface marine heatwaves in an eddy-resolving model

A shift in depth range enables marine organisms to adapt to marine heatwaves (MHWs). Subsurface MHWs could limit this pathway, yet their response to climate warming remains unclear. Here, using an eddy-resolving Earth system model forced under a high emission scenario, we project a robust global increase in subsurface MHWs driven by rising subsurface mean temperatures and enhanced temperature variability. Historically, maximum MHW intensity occurs around 100m depth, which shifts to the faster-warming surface under greenhouse warming. However, removing the long-term warming trend yields an increase in subsurface MHW intensity and annual days greater than that at the surface, especially in large marine ecosystem regions, primarily due to increased variability. Additionally, days of the surface and subsurface concurrent event increase ten times more than those of individual events. Our study highlights a heightened threat to marine organisms under global warming, as the increased subsurface heatwaves reduce their refuge options.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Using Information Theory to Evaluate Directional Precipitation Interactions Over the West Sahel Region in Observations and Models

Water availability has historically been one of the most significant threats to African regional social and economic well-being. Over the Sahel region, a megadrought during the 1960s and 1970s induced by an abrupt and substantial rainfall reduction caused widespread famine and death. The postdrought recovery, which is still ongoing, has been characterized by gradual increases in rainfall, but with dramatic fluctuations. The large negative human impacts, slow recovery, and variability raise important questions of why and how rainfall dynamics evolve and interact with other components of the regional climate system. Here we provide an observational assessment of rainfall interactions mechanisms (informed by directional transfer of information entropy) that regulate Sahel rainfall. We quantitatively demonstrate that (1) sea surface temperature over the Gulf of Guinea controls moisture advection and transport to the West Sahel region and (2) strong bidirectional interactions exist between local vegetation dynamics and rainfall patterns. Then we assess the directional interaction patterns from nine state-of-the-art Earth System Models (ESMs). We find that most ESMs are able to represent either the unidirectional control of sea surface temperature on precipitation or the bidirectional interaction between vegetation and precipitation. However, none of the ESMs represents both interactive patterns. The GFDL and IPSL-CM5A-LR models successfully reproduced observed patterns over ~50% of the West Sahel region but were not accurate in reproducing observed precipitation regional trends or interannual variation. We propose that the directional information transfer is a powerful mechanistic benchmark to assess model fidelity at the process level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

ELM-Wet: Inclusion of a wetland landunit with sub-grid representation of eco-hydrological patches and hydrological forcing in E3SM Land Model (ELM)

Wetlands emit the most biogenic methane (CH4) and present the greatest uncertainty in the global CH4 budget. Modeling these emissions is challenging due to the temporal and spatial variability in wetland structure and CH4 flux rates, along with complex interactions among hydrological, ecological, meteorological, and microbial processes that govern CH4 dynamics. To address these issues, we aim to enhance the accuracy of wetland representation in the U.S. Department of Energy’s Exascale Earth System Model (E3SM) Land Model, ELM. This effort led to the development of ELM-Wet, which incorporates a dedicated wetland landunit with subgrid representation of eco-hydrological patch types. We implement wetland-specific hydrology by imposing site-specific constraints on surface water levels, thereby allowing different patches to sustain varying depths of inundation. Additionally, we refined the calculation of aerenchyma transport diffusivity based on observed conductance across different vegetation types. We validated these enhancements through site-specific simulations of a coastal freshwater wetland, the Salvador WMA Freshwater Marsh (Ameriflux, site ID US-LA2), located in the coast of Louisiana (29.85N,90.29W) at an elevation of 0 m. The site was simulated with ELM-Wet and the default version ELMv1. We use Bayesian Optimization to parameterize CO2 and CH4 fluxes. Eddy covariance observations of CO2 and CH4 fluxes from 2012-2013 were used to train the model and data from 2021 were used for validation. In this repository, we include all the output of all the simulations performed using different versions of ELMv1 and ELM-Wet, all input required to the model, and the Matlab scripts we used to processed the output data. ELM-Wet_flmd.csv includes a detailed description of the datasets files.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Scientific Data From Precipitation Driver Response Model Intercomparison Project

This data descriptor reports the main scientific values from General Circulation Models (GCMs) in the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). The purpose of the GCM simulations has been to enhance the scientific understanding of how changes in greenhouse gases, aerosols, and incoming solar radiation perturb the Earth’s radiation balance and its climate response in terms of changes in temperature and precipitation. Here we provide global and annual mean results for a large set of coupled atmospheric-ocean GCM simulations and a description of how to easily extract files from the dataset. The simulations consist of single idealized perturbations to the climate system and have been shown to achieve important insight in complex climate simulations. We therefore expect this data set to be valuable and highly used to understand simulations from complex GCMs and Earth System Models for various phases of the Coupled Model Intercomparison Project

Precipitation Driver Response Model Intercompariso↗
NASA NTRS2022

Model Evaluation of Short-Lived Climate Forcers for the Arctic Monitoring and Assessment Programme: A Multi-Species, Multi-Model Study

While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008-2009 and 2014-2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH4, 03, BC, and SO(sup 2-)(sub 4)), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for C"4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.

Arctic Monitoring and Assessment Programme↗
DOE OSTI2016

Integrating Climate and Risk-Informed Science to Support Critical Decisions

The PNNL Environmental Health and Remediation Sector stewards several decision support capabilities to integrate climate- and risk-informed science to support critical decisions. Utilizing our expertise in risk and decision analysis, integrated Earth systems modeling, and remote sensing and geoinformatics, PNNL is influencing the way science informs high level decisions at national, regional and local scales to protect and preserve our most critical assets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRSDate not supplied

Coupling of Dust Mineralogy to Radiation in NASA GISS ModelE: Impact Upon Dust Optical Properties and Direct Radiative Effect

Estimates of dust direct radiative effect (DRE) are still largely uncertain mainly due to the poor constraining of regional variations of dust absorption, whose known connection with mineral composition is still largely neglected by most Earth System Models. The inaccurate knowledge of soil mineralogy and insufficient measurements of dust physical and optical properties still present the main challenges for a proper representation of the dust-radiation interaction in climate models. We recently upgraded the coupling of dust minerals to shortwave radiation in the NASA GISS ModelE. In particular, we extended the interaction to online-calculated minerals and implemented two different mixing configurations: i) external mixing of three main mineral components and ii) internal mixing of all minerals, allowing a dynamic fraction of iron oxides. We used a semi-empirical approach for the optical calculations, mostly based on aerosol chamber measurements from natural soils, aiming to reduce the influence of uncertain assumptions on the optical properties and mixing state of individual minerals. In this contribution, we present the results of our first calculations of dust optical properties and DREs based on different mixing configurations of interactive minerals. We show the changes to dust optical depth and single scattering albedo, with respect to the control case using homogeneous dust composition, and the consequent global and regional variations in dust DREs at the top of atmosphere and at surface. We also compare the model optical properties with AERONET data, filtered to identify dust scenes. We find that accounting for the diverse properties of dust minerals modifies the pattern of regional dust absorption and therefore its impact upon climate. We also show that the mineralogy effect is sensitive to the assumed mixing state of minerals.

dust direct radiative effect↗
DOE OSTI2022

Root Foraging Alters Global Patterns of Ecosystem Legacy From Climate Perturbations

The response of terrestrial ecosystems to climate perturbations typically persist longer than the timescale of the forcing, a phenomenon broadly referred to as legacy. Understanding the strength of legacy is critical for predicting ecosystem sensitivity to climate extremes and the extent that persistent changes in surface-atmosphere exchange might feedback onto climate. The cause of ecosystem legacy has been associated with myriad factors such as changes in aboveground biomass, however, few studies have tested how changes in the depth distribution of fine roots in response to perturbation might alter an ecosystem's recovery time. We explore this question using an Earth System Model that includes a dynamic root module where vegetation can forage for water and nutrients by altering their root profiles. The global simulations presented here show that in response to water stress events most ecosystems deepen their root profiles. In semi-arid ecosystems, this response expedites recovery (i.e., less legacy) relative to simulations without dynamics roots because access to deeper water pools after the initial event remains favorable. In wetter ecosystems, the development of deeper root profiles slows down the recovery timescale (i.e., more legacy) because the deeper root profile reduces access to nutrients and is thus unfavorable. Here, the simulations show that while the inclusion of dynamic roots might only minimally affect global patterns of Gross Primary Productivity and Evapotranspiration, the shift in root profile alters the timescale of recovery. Studies interested in the sustained response of land surfaces fluxes to climate disturbances should consider models that include dynamic root capability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Forest response to increased disturbance in the central Amazon and comparison to western Amazonian forests

Uncertainties surrounding vegetation response to increased disturbance rates associated with climate change remains a major global change issue for Amazonian forests. Additionally, turnover rates computed as the average of mortality and recruitment rates in the western Amazon basin are doubled when compared to the central Amazon, and notable gradients currently exist in specific wood density and aboveground biomass (AGB) between these two regions. This study investigates the extent to which the variation in disturbance regimes contributes to these regional gradients. To address this issue, we evaluated disturbance–recovery processes in a central Amazonian forest under two scenarios of increased disturbance rates using first ZELIG-TROP, a dynamic vegetation gap model which we calibrated using long-term inventory data, and second using the Community Land Model (CLM), a global land surface model that is part of the Community Earth System Model (CESM). Upon doubling the mortality rate in the central Amazon to mirror the natural disturbance regime in the western Amazon of ~2% mortality, the two regions continued to differ in multiple forest processes. With the inclusion of elevated natural disturbances, at steady state, AGB significantly decreased by 41.9% with no significant difference between modeled AGB and empirical AGB from the western Amazon data sets (104 vs. 107 Mg C ha -1 , respectively). However, different processes were responsible for the reductions in AGB between the models and empirical data set. The empirical data set suggests that a decrease in wood density is a driver leading to the reduction in AGB. While decreased stand basal area was the driver of AGB loss in ZELIG-TROP, a forest attribute that does not significantly vary across the Amazon Basin. Further comparisons found that stem density, specific wood density, and basal area growth rates differed between the two Amazonian regions. Last, to help quantify the impacts of increased disturbances on the climate and earth system, we evaluated the fidelity of tree mortality and disturbance in CLM. Similar to ZELIG-TROP, CLM predicted a net carbon loss of 49.9%, with an insignificant effect on aboveground net primary productivity (ANPP). Decreased leaf area index (LAI) was the driver of AGB loss in CLM, another forest attribute that does not significantly vary across the Amazon Basin, and the temporal variability in carbon stock and fluxes was not replicated in CLM. Our results suggest that (1) the variability between regions cannot be entirely explained by the variability in disturbance regime, but rather potentially sensitive to intrinsic environmental factors; or (2) the models are not accurately simulating all tropical forest characteristics in response to increased disturbances.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

A theoretical index for understanding distinct land relative humidity trends in observations, reanalyses, and models

Land surface relative humidity (RH) is a key variable in the coupled land–atmosphere system that profoundly influences terrestrial hydroclimate and ecosystems. Yet historical changes in land RH are not well understood due to limited observations, biased reanalyses, and the lack of a framework for interpreting RH changes under multiple influencing factors. Here, we show that the spatiotemporal variability of land RH and its distinct historical trends among observations, reanalyses, and Earth system models are captured by a simple index based on the ratio of precipitation (P) to a modified potential evapotranspiration formulated independently of RH (PET°). The index provides a physical calibration of biased land RH in reanalyses and a quantitative framework for interpreting land RH changes. Over 1973–2024, land RH has decreased substantially, owing to the intrinsic rise in PET° with temperature and little increase in land precipitation. Reanalyses overestimate the observed RH decrease, consistent with exaggerated surface warming and precipitation decline. The index captures this coherent bias and enables a calibration using observed precipitation and temperature. Models simulate a wide range of land RH trends, but nearly all runs underrepresent the historical drying. The index captures the model spread and discrepancy and attributes them to contributions of precipitation and PET°. Weaker land RH decreases in models arise mainly from weaker subtropical precipitation declines, linked to muted intensification of subtropical highs and biased subtropical climatology. The model–observation discrepancy is unlikely explained by internal variability, implying model underestimation of forced RH decrease and a drier land future than current projections.

Earth system models↗