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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Convective-To-Total Precipitation Ratio and the “Drizzling” Bias in Climate Models

Overestimation of precipitation frequency and duration while underestimating intensity, that is, the “drizzling” bias, has been a long-standing problem of global climate models. Here we explore this issue from the perspective of precipitation partitioning. We found that most models in the Climate Model Intercomparison Project Phase 5 (CMIP5) have high convective-to-total precipitation (PC/PR) ratios in low latitudes. Convective precipitation has higher frequency and longer duration but lower intensity than non-convective precipitation in many models. As a result, the high PC/PR ratio contributes to the “drizzling” bias over low latitudes. The PC/PR ratio and associated “drizzling” bias increase as model resolution coarsens from 0.5° to 2.0°, but the resolution's effect weakens as the grid spacing increases from 2.0° to 3.0°. Some of the CMIP6 models show reduced “drizzling” bias associated with decreased PC/PR ratio. Thus, more reasonable precipitation partitioning, along with finer model resolution should alleviate the “drizzling” bias within current climate models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Climate-Driven Limits to Future Carbon Storage in California's Wildland Ecosystems

Enhanced ecosystem carbon storage is a key component of many climate mitigation pathways. The State of California has set an ambitious goal of carbon neutrality by 2045, relying in part on enhanced carbon sequestration in natural and working lands. We used statistical modeling, including random forest and climate analog approaches, to explore the climate-driven challenges and uncertainties associated with the goal of long-term carbon sequestration in forests and shrublands. We found that seasonal patterns of temperature and precipitation are strong controllers of the spatial distribution of aboveground live carbon. RCP8.5 projections of temperature and precipitation are estimated to drive decreases of 16.1% ± 7.5% in aboveground live carbon by the end of the century, with coastal areas of central and northern California and low/mid-elevation mountain areas being most vulnerable. With RCP4.5 projections, declines are less severe, with 8.8% ± 5.3% carbon loss. In either scenario, increases in temperature systematically cause biomass declines, and the spread of projected precipitation across 32 CMIP5 models contributes to substantial uncertainty in the magnitude of that decline. Projected changes in the environmental niche for the 20 most biomass-dominant tree species revealed widespread replacement of conifers by oak species in low elevation regions of central and northern California, with a corresponding decline in carbon storage depending on expected migration rates. The spatial patterns of vulnerability we identify may allow policymakers to assess where carbon sequestration in aboveground biomass is an appropriate part of a climate mitigation portfolio, and where future climate-driven carbon losses may be a liability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multiple Metrics Informed Projections of Future Precipitation in China

Predicting how regional precipitation will respond to future warming is among the most challenging undertaking in climate change projection. Despite sustained efforts to improve modeling and understanding of precipitation, the overall uncertainty in projecting regional precipitation has not been reduced substantially. In this work, the potential for more robust precipitation projections is demonstrated through the use of discriminating metrics to subsample the multimodel ensemble. Using a two-dimensional metric of precipitation and its relationship with large-scale circulation indices in East Asia, 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) are classified into three groups. Models in the top performing group projected statistically significant increasing trends in precipitation and the regional precipitation patterns are more similar to each other than to the patterns in the bottom performing group. In contrast, models in the bottom performing group projected diverse responses, with overall small drying or no significant trends in precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback

The surprising increase of Earth's climate sensitivity in the most recent coupled model intercomparison project (CMIP) models has been largely attributed to extratropical cloud feedback, which is thought to be driven by greater supercooled water in present-day cloud phase partitioning (CPP). Here, we report that accounting for precipitation in the Goddard Institute for Space Studies ModelE3 radiation scheme, neglected in more than 60% of CMIP6 and 90% of CMIP5 models, systematically changes its apparent CPP and substantially increases its cloud feedback, consistent with results using CMIP models. Including precipitation in the comparison with cloud–aerosol lidar and infrared pathfinder satellite observations (CALIPSO) measurements and in model radiation schemes is essential to faithfully constrain cloud amount and phase partitioning, and simulate cloud feedbacks. Our findings suggest that making radiation schemes precipitation-aware (missing in most CMIP6 models) should strengthen their positive cloud feedback and further increase their already high mean climate sensitivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Does Regional Hydroclimate Change Scale Linearly With Global Warming?

Many aspects of climate change scale linearly with global warming. However, nonlinear changes are possible, especially in the context of hydroclimate, and under emissions scenarios with stabilized global temperature, as aspired to by current climate targets. In CMIP5 and 6, a progressively larger land area shows nonlinear changes as a function of global warming when considering precipitation, evaporation, and soil moisture, with the latter showing nonlinearity over ~50% of global land. Here, using ensemble simulations with the Community Earth System Model 1, in which individual forcing factors are held constant, we illustrate how nonadditive responses to anthropogenic greenhouse gases and industrial and fire-related aerosols can yield complex soil moisture changes in certain regions. This complexity contributes to uncertainty in regional soil moisture projections and suggests that the timing of, as well as model response uncertainty to, future aerosol reductions will have significant impacts on regional hydroclimate change as global temperatures stabilize.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Increased Variability of Biomass Burning Emissions in CMIP6 Amplifies Hydrologic Cycle in the CESM2 Large Ensemble

Abstract Historical simulations performed for the Coupled Model Intercomparison Project Phase 6 used biomass burning emissions between 1997 and 2014 containing higher spatial and temporal variability compared to emission inventories specified for earlier years, and compared to emissions used in previous (e.g., CMIP5) simulation intercomparisons. Using the Community Earth System Model version 2 Large Ensemble, we show this increased biomass burning emissions variability leads to amplification of the hydrologic cycle poleward of 40°N. Notably, the high variability of biomass burning emissions leads to increased latent heat fluxes, column‐integrated precipitable water, and precipitation. Greater ocean heat uptake, weaker meridional energy transport from the tropics, greater atmospheric shortwave and longwave absorption, and lower relative humidity act to moderate this hydrologic cycle amplification. Our results suggest it is not only the secular changes (on multidecadal timescales) in biomass burning emissions that impact the hydrologic cycle, but also the shorter timescale variability in emissions.

Heyblom, Kyle B.↗
DOE OSTI2022

On the Correspondence Between Atmosphere-Only and Coupled Simulations for Radiative Feedbacks and Forcing From CO 2

Atmosphere-only experiments are widely used to investigate climate feedbacks simulated in more computationally expensive fully coupled global climate model simulations. We confirm that this remains a valid approach by comparing the radiative feedbacks and forcing between coupled and atmosphere-only simulations for the latest models taking part in the 6th phase of the Coupled Model Intercomparison Project (CMIP6). For global-mean cloud feedbacks, we find a better than previously known correspondence between these experiments, which applies even to the response of individual cloud properties (amount, altitude, and optical depth) and holds even when considering atmosphere-only simulations of only 1 yr duration. For regional cloud feedbacks, the correspondence between the two experiments is generally present at every geographic location except for the tropical Pacific but takes longer experiments to reveal. For the lapse rate and surface albedo feedbacks, the correspondence between the two experiments is weaker due to the non-uniform warming pattern and loss of sea ice in the coupled experiment. For the across-model relationship between 4xCO 2 effective radiative forcing and feedback, we find a different behavior across experiments in CMIP6 than in CMIP5, casting doubt on the physical significance of previous results that highlighted an anti-correlation between the two quantities. Overall, these results confirm the utility of atmosphere-only experiments particularly to study cloud feedbacks, which are the dominant source of inter-model spread in climate sensitivity.

58 GEOSCIENCES↗
DOE OSTI2022

Impacts of Suppressing Excessive Light Rain on Aerosol Radiative Effects and Health Risks

Global climate models (GCMs) have been used widely to study radiative forcing and health risks of aerosols. A recent study using two GCMs found that light rain plays a dominant role in controlling aerosol loading. However, “too much light rain and too little heavy rain” is a longstanding bias in GCMs. It is unclear how much light rain affects aerosol-cloud-radiation interactions and health risks from air pollution. Here, in this work, we show that, with the correction of the rainfall intensity spectrum in the National Center for Atmospheric Research Community Atmosphere Model version 5.3 by introducing a stochastic deep convection scheme, the reduced frequency of light rain (1–20 mm d -1 ) results in changes of aerosol direct radiative effects (DRE) of up to -0.5 ± 0.03 W/m 2 and aerosol cloud radiative effects (CRE) of up to -0.9 ± 0.03 W/m 2 . The total (CRE + DRE) radiative effects of light rain-mediated aerosol changes exceed the present-day anthropogenic forcing of aerosols relative to preindustrial levels from the Coupled Model Intercomparison Project (CMIP5&6) models. However, the correction of the rainfall intensity spectrum has little effect on anthropogenic aerosol forcing (defined as the radiative perturbation due to changes in aerosol concentrations between the industrial era and preindustrial levels). Due to increased exposure to fine particulates (PM 2.5 ), the estimated global total premature mortality is much higher than previously estimated, by 300,000 ± 60,000 deaths per year, and is more severe in populous regions such as India and China. The findings in this study highlight the need to understand uncertainties in radiative effects and health risks of aerosols due to simulation biases of precipitation in GCMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Estimating the Likelihood of GHG Concentration Scenarios From Probabilistic Integrated Assessment Model Simulations

The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO 2 ) concentration scenarios used in key climate change modeling experiments. It computes the CO 2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO 2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO 2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5-8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO 2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Aridity-Dependent Land Surface Skin Temperature Biases in CMIP5/6

Land surface skin temperature, a critical indicator of climate change, connects the water and energy cycles between the land and the atmosphere. Here, in this study, we evaluate the simulations of land surface skin temperature from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models with satellite-based datasets and reanalysis. We find systematic cold skin temperature biases over arid regions in CMIP5/CMIP6 simulations. Over arid and semi-arid regions, latent heat biases drive skin temperature biases by evaporative cooling. Over humid regions, surface downward shortwave and albedo biases are relatively more critical. Spatial patterns of biases remain similar in the latest CMIP6 simulations, suggesting systematic biases in land-atmosphere interactions. These biases need to be corrected or considered while using models for future projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Roles of External Forcing and Internal Variability in Precipitation Changes of a Sub-Region of the U.S. Mid-Atlantic During 1979–2019

Observations of the seasonal mean precipitation averaged over a sub-region of the U.S. mid-Atlantic show significant increases in summer but no changes in winter during 1979–2019. Clarifying the drivers of these observed changes is important for understanding and interpreting future projections of regional precipitation by climate models. Herein, this study examines the relative effects of external forcing and internal variability in the observed multi-decadal changes using the historical and RCP8.5/SSP585 simulations from 37 models in the Coupled Model Intercomparison Project phase 5 (CMIP5), 35 models in CMIP6, as well as large ensemble simulations from 6 models, each including more than 20 members. During 1979–2019, enhanced warming over land relative to ocean due to external forcing induces low pressure anomalies over land that contribute to the wetting summer. While similar wetting also occurs during winter, such effect is offset by the drying associated with the positive phase of the North Atlantic Oscillation (NAO) in recent decades. Internal variability produces large uncertainty in the multi-decadal precipitation changes simulated by climate models through changes in large-scale circulation associated with the NAO in winter and the North Atlantic Subtropical High in summer. These multi-decadal relationships between precipitation and large-scale circulation under internal variability also manifest on inter-annual timescale. These relationships highlight enhanced moisture transport from the Gulf of Mexico and North Atlantic in winter and near the mid-Atlantic coast in summer that modulates precipitation change in the study region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Climate Change Signal in Atlantic Tropical Cyclones Today and Near Future

This manuscript discusses the challenges in detecting and attributing recently observed trends in the Atlantic tropical cyclone (TC) and the epistemic uncertainty we face in assessing future risk. We use synthetic storms downscaled from five CMIP5 models by the Columbia HAZard model (CHAZ), and directly simulated storms from high-resolution climate models. We examine three aspects of recent TC activity: the upward trend and multi-decadal oscillation of the annual frequency, the increase in storm wind intensity, and the decrease in forward speed. Some data sets suggest that these trends and oscillation are forced while others suggest that they can be explained by natural variability. Projections under warming climate scenarios also show a wide range of possibilities, especially for the annual frequencies, which increase or decrease depending on the choice of moisture variable used in the CHAZ model and on the choice of climate model. The uncertainties in the annual frequency lead to epistemic uncertainties in TC risk assessment. Here, we investigate the potential for reduction of these epistemic uncertainties through a statistical practice, namely likelihood analysis. We find that historical observations are more consistent with the simulations with increasing frequency than those with decreasing frequency, but we are not able to rule out the latter. We argue that the most rational way to treat epistemic uncertainty is to consider all outcomes contained in the results. In the context of risk assessment, since the results contain possible outcomes in which TC risk is increasing, this view implies that the risk is increasing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Radiative, Hydrologic, and Circulation Responses to Warming in Cess‐Potter Simulations Using the Global 3.25‐km SCREAM

Using the global 3.25-km Simple Cloud Resolving E3SM Atmosphere Model (SCREAM 3 km), a pair of 13-month Cess-Potter simulations are performed to quantify the radiative feedbacks and the hydrologic and circulation responses to warming. Large-scale aspects of SCREAM 3 km's top-of-atmosphere radiative fluxes, precipitation rates, and circulations are in good agreement with observations and reanalysis, with notable differences, including a drier lower free-troposphere in the Tropics, reduced precipitation and humidity over the Tropical West Pacific, and poleward shifted Southern Hemisphere midlatitude jet. In response to warming, SCREAM 3 km predicts a total radiative feedback within the top 15% of the CMIP5 and CMIP6 models, which puts it substantially higher than the feedback reported by other kilometer-scale models. SCREAM 3 km's high radiative feedback stems from a strongly positive shortwave cloud feedback, most prominent over the mid- and high-latitudes. SCREAM 3 km's high precipitation response also puts it among the highest of CMIP models, whereas its circulation response is within the spread of CMIP models. An ensemble of five perturbed initial condition Cess-Potter simulations with a 12 km version of SCREAM (SCREAM 12 km) is performed to characterize uncertainty and resolution sensitivity. It suggests that the uncertainty from analyzing a pair of 1-year simulations is small compared to the inter-model spread in feedbacks and precipitation response. SCREAM 12 km also produces a strong precipitation response to warming but a much lower cloud feedback and total radiative feedback. The results from these experiments suggest that the spread in climate feedbacks will likely persist in the next generation of kilometer-scale models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating Mean State Cloud Properties in the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM)

Accurately simulating clouds remains a key challenge in global climate models, primarily because cloud formation involves sub-grid processes that are parameterized and crudely represented in models. This study examines the performance of DOE's Simple Cloud-Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) in simulating cloud properties and their spatio-temporal distribution by comparing against satellite observations. Two horizontal resolutions of SCREAM (3 and 12 km) are examined, and both depict a realistic spatial structure of mean-state cloud cover but underestimate its global mean magnitude. SCREAM 3 km reasonably reproduces the distribution of mean-state cloud properties across various cloud optical thickness and cloud-top pressure regimes, with performance comparable to CMIP5 and CMIP6 ensemble and marginally outperforming SCREAM 12 km. Still, SCREAM 3 km tends to underpredict low clouds and optically thin clouds, highlighting the need for continued improvement in representing unresolved processes. This study provides a basis for confidence in the representation of clouds in SCREAM, as simulating mean-state clouds is a necessary prerequisite for trusting its cloud responses to changes in aerosols and greenhouse gases.

Geosciences↗
DOE OSTI2020

Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation

Both seasonal and annual mean precipitation and evaporation influence patterns of water availability impacting society and ecosystems. Existing global climate studies rarely consider such patterns from non-parametric statistical standpoint. Here, we employ a non-parametric analysis framework to analyze seasonal hydroclimatic regimes by classifying global land regions into nine regimes using late 20th century precipitation means and seasonality. These regimes are used to assess implications for water availability due to concomitant changes in mean and seasonal precipitation and evaporation changes using CMIP5 model future climate projections. Out of 9 regimes, 4 show increased precipitation variation, while 5 show decreased evaporation variation coupled with increasing mean precipitation and evaporation. Increases in projected seasonal precipitation variation in already highly variable precipitation regimes gives rise to a pattern of “seasonally variable regimes becoming more variable”. Regimes with low seasonality in precipitation, instead, experience increased wet season precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A spatial emergent constraint on the sensitivity of soil carbon turnover to global warming

Carbon cycle feedbacks represent large uncertainties in climate change projections, and the response of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil carbon depend on changes in litter and root inputs from plants and especially on reductions in the turnover time of soil carbon (τ s ) with warming. An approximation to the latter term for the top one metre of soil (ΔC s,τ ) can be diagnosed from projections made with the CMIP6 and CMIP5 Earth System Models (ESMs), and is found to span a large range even at 2 °C of global warming (-196 ± 117 PgC). Here, we present a constraint on ΔC s,τ , which makes use of current heterotrophic respiration and the spatial variability of τs inferred from observations. This spatial emergent constraint allows us to halve the uncertainty in ΔC s,τ at 2 °C to -232 ± 52 PgC.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Diminishing seasonality of subtropical water availability in a warmer world dominated by soil moisture–atmosphere feedbacks

Global warming is expected to cause wet seasons to get wetter and dry seasons to get drier, which would have broad social and ecological implications. However, the extent to which this seasonal paradigm holds over land remains unclear. Here we examine seasonal changes in surface water availability (precipitation minus evaporation, P–E) from CMIP5 and CMIP6 projections. While the P–E seasonal cycle does broadly intensify over much of the land surface, ~20% of land area experiences a diminished seasonal cycle, mostly over subtropical regions and the Amazon. Using land–atmosphere coupling experiments, we demonstrate that 63% of the seasonality reduction is driven by seasonally varying soil moisture (SM) feedbacks on P–E. Declining SM reduces evapotranspiration and modulates circulation to enhance moisture convergence and increase P–E in the dry season but not in the wet season. Our results underscore the importance of SM–atmosphere feedbacks for seasonal water availability changes in a warmer climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

54 ENVIRONMENTAL SCIENCES↗