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Po-Chedley, Stephen

Publications and source records attributed to Po-Chedley, Stephen.

17 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

xCDAT: A Python Package for Simple and Robust Analysis of Climate Data

xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray (Hoyer & Hamman, 2017) for climate data analysis on structured grids. xCDAT streamlines analysis of climate data by exposing common climate analysis operations through a set of straightforward APIs. Some of xCDAT’s key features include spatial averaging, temporal averaging, and regridding. These features are inspired by the Community Data Analysis Tools (CDAT) library (Dean N. Williams et al., 2009) (D. N. Williams, 2014) (Doutriaux et al., 2019) and leverage powerful packages in the Xarray ecosystem including xESMF (Zhuang et al., 2023), xgcm (Abernathey et al., 2022), and CF xarray (Cherian et al., 2023). To ensure general compatibility across various climate models, xCDAT operates on datasets that are compliant with the Climate and Forecast (CF) metadata conventions (Hassell et al., 2017).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)—key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO 2 forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

58 GEOSCIENCES↗
DOE OSTI2023

Exceptional stratospheric contribution to human fingerprints on atmospheric temperature

In 1967, scientists used a simple climate model to predict that human-caused increases in atmospheric CO 2 should warm Earth’s troposphere and cool the stratosphere. This important signature of anthropogenic climate change has been documented in weather balloon and satellite temperature measurements extending from near-surface to the lower stratosphere. Stratospheric cooling has also been confirmed in the mid to upper stratosphere, a layer extending from roughly 25 to 50 km above the Earth’s surface (S 25 − 50 ). To date, however, S 25 − 50 temperatures have not been used in pattern-based attribution studies of anthropogenic climate change. Here, we perform such a “fingerprint” study with satellite-derived patterns of temperature change that extend from the lower troposphere to the upper stratosphere. Including S 25 − 50 information increases signal-to-noise ratios by a factor of five, markedly enhancing fingerprint detectability. Key features of this global-scale human fingerprint include stratospheric cooling and tropospheric warming at all latitudes, with stratospheric cooling amplifying with height. In contrast, the dominant modes of internal variability in S 25 − 50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S 25 − 50 signal and noise patterns are accompanied by large cooling of S 25 − 50 (1 to 2 ° C over 1986 to 2022) and low S 25 − 50 noise levels. Our results explain why extending “vertical fingerprinting” to the mid to upper stratosphere yields incontrovertible evidence of human effects on the thermal structure of Earth’s atmosphere.

climate modeling↗
DOE OSTI2022

Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming

Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979. The causes of this difference are not fully understood and are poorly quantified. Here, we apply machine learning to relate the patterns of surface-temperature change to the forced and unforced components of tropical tropospheric warming. This approach allows us to disentangle the forced and unforced change in the model-simulated temperature of the midtroposphere (TMT). In applying the climate-model-trained machine-learning framework to observations, we estimate that external forcing has produced a tropical TMT trend of 0.25 ± 0.08 K⋅decade −1 between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 K⋅decade −1 . Using the Community Earth System Model version 2 (CESM2) large ensemble, we also find that a discontinuity in the variability of prescribed biomass-burning aerosol emissions artificially enhances simulated tropical TMT change by 0.04 K⋅decade −1 . The magnitude of this aerosol-forcing bias will vary across climate models, but since the latest generation of climate models all use the same emissions dataset, the bias may systematically enhance climate-model trends over the satellite era. Our results indicate that internal variability and forcing uncertainties largely explain differences in satellite-versus-model warming and are important considerations when evaluating climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Robust Anthropogenic Signal Identified in the Seasonal Cycle of Tropospheric Temperature

Previous work identified an anthropogenic fingerprint pattern in T AC (x, t), the amplitude of the seasonal cycle of mid- to upper-tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite T AC (x, t) data could have been influenced by real-world multidecadal internal variability (MIV). Here we address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their T AC (x, t) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3–4) intermodel and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal T AC (x, t) variability associated with the Atlantic multidecadal oscillation and El Niño–Southern Oscillation. The robustness of the seasonal cycle detection and attribution results shown here, taken together with the evidence from idealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced T AC (x, t) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Advancing the Predictability of Water Cycle Phenomena via the Application of AI to Model Ensemble Simulations and Observations

AI has the power to identify new pathways to extended predictability of the water cycle via its application to model ensembles together with accompanying observations. We discuss both scientific and technological aspects of this challenge, and address the parts of the MODEX approach involving “Model simulations, evaluation, analysis, and benchmarking” and “Identification of key knowledge gaps”. The widespread incorporation of AI into model analysis will significantly advance Earth system predictability and our predictive capabilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Natural variability contributes to model–satellite differences in tropical tropospheric warming

Significance Climate models have, on average, simulated substantially more tropical tropospheric warming than satellite data, with few simulations matching observations. It has been suggested that this discrepancy arises because climate models are overly sensitive to greenhouse gas increases. Tropical tropospheric temperature trends from a large ensemble of simulations performed with a single climate model span a wide range that is solely due to natural climate variability. A subset of these simulations have warming rates in accord with satellite observations. Simulations with diminished tropical tropospheric warming due to climate variability exhibit characteristic patterns of surface warming that are similar to the observed record. Our results indicate that multidecadal variability can explain current model–observational differences in the rate of tropical tropospheric warming.

58 GEOSCIENCES↗
DOE OSTI2020

Advancing Measurements and Understanding of the Rate and Structure of Atmospheric Warming

The rate and geographic pattern of atmospheric warming are key indicators of historical climate change and play a role in modulating future changes in Earth’s climate. The past and future evolution of atmospheric temperature is simulated using global climate models while observational estimates of past warming are derived from satellite microwave measurements. Individual climate models simulate widely varying rates of past and future atmospheric warming and most model simulations exhibit greater tropospheric (lowest ~10 km of atmosphere) temperature change than satellite observations between 1979 and 2020. This project examined intermodel differences in the pattern of atmospheric warming and how these differences influence climate feedbacks that can amplify or damp the rate of global surface warming. A key result is that climate model representation of the current climate can influence climate feedbacks and the simulation of future changes in climate. Another focus of this project was to analyze how models respond to different input datasets, such as volcanic aerosols or sea surface temperature (in atmosphere-only simulations). We find that different prescribed inputs can affect simulated changes in atmospheric temperature, even though “input uncertainty” is often unconsidered in model-observational comparisons. A final focus of this research project was to consider the influence of natural internal climate variability on satellite era changes in climate. Although the Earth is warming substantially due to anthropogenic emissions of greenhouse gases, the observed warming rate can be modulated by natural variations in the Earth’s climate. We find that natural climate variability has slowed the rate of tropical tropospheric warming, which explains model-satellite differences in the rate of tropospheric warming.

58 GEOSCIENCES↗
DOE OSTI2020

Quantifying CanESM5 and EAMv1 sensitivities to Mt. Pinatubo volcanic forcing for the CMIP6 historical experiment

Large volcanic eruptions reaching the stratosphere have caused marked perturbations to the global climate including cooling at the Earth's surface, changes in large-scale circulation and precipitation patterns and marked temporary reductions in global ocean heat content. Many studies have investigated these effects using climate models; however, uncertainties remain in the modelled response to these eruptions. This is due in part to the diversity of forcing datasets that are used to prescribe the distribution of stratospheric aerosols resulting from these volcanic eruptions, as well as uncertainties in optical property derivations from these datasets. To improve this situation for the sixth phase of the Coupled Model Intercomparison Project (CMIP6), a two-step process was undertaken. First, a combined stratospheric aerosol dataset, the Global Space-based Stratospheric Aerosol Climatology (GloSSAC; 1979–2016), was constructed. Next, GloSSAC, along with information from ice cores and Sun photometers, was used to generate aerosol distributions, characteristics and optical properties to construct a more consistent stratospheric aerosol forcing dataset for models participating in CMIP6. This “version 3” of the stratospheric aerosol forcing has been endorsed for use in all contributing CMIP6 simulations. Recent updates to the underlying GloSSAC from version 1 to version 1.1 affected the 1991–1994 period and necessitated an update to the stratospheric aerosol forcing from version 3 to version 4. As version 3 remains the official CMIP6 input, quantification of the impact on radiative forcing and climate is both relevant and timely for interpreting results from experiments such as the CMIP6 historical simulations. This study uses two models, the Canadian Earth System Model version 5 (CanESM5) and the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1), to estimate the difference in instantaneous radiative forcing in simulated post-Pinatubo climate response when using version 4 instead of version 3. Differences in temperature, precipitation and radiative forcings are generally found to be small compared to internal variability. An exception to this is differences in monthly temperature anomalies near 24 km altitude in the tropics, which can be as large as 3 °C following the eruption of Mt. Pinatubo.

58 GEOSCIENCES↗
DOE OSTI2019

Celebrating the anniversary of three key events in climate change science

Climate science celebrates three 40th anniversaries in 2019: the release of the Charney report, the publication of a key paper on anthropogenic signal detection, and the start of satellite temperature measurements. This confluence of scientific understanding and data led to the identification of human fingerprints in atmospheric temperature. The 1970s saw growing concern about the potential for anthropogenic climate change. In this paper, we focus on understanding how the scientific advances arising from the three anniversary events aided efforts to identify human influences on the thermal structure of the atmosphere.

58 GEOSCIENCES↗
DOE OSTI2018

Natural Variability Has Slowed the Decline in Western U.S. Snowpack Since the 1980s

Spring snowpack in the mountains of the western United States has not declined substantially since the 1980s, despite significant global and regional warming. Here in this paper we show that this apparent insensitivity of snowpack to warming is a result of changes in the atmospheric circulation over the western United States, which have reduced snowpack losses due to warming. Climate model simulations indicate that the observed circulation changes have been driven in part by a shift in Pacific sea surface temperatures that is attributable to natural variability, and not part of the simulated response to anthropogenic forcing. Removing the influence of natural variability reveals a robust anthropogenic decline in western U.S. snowpack since the 1980s, particularly during the early months of the accumulation season (October–November). These results suggest that the recent stability of western U.S. snowpack will be followed by a period of accelerated decline once the current mode of natural variability subsides.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Forced temperature changes contaminate a natural variability constraint on climate sensitivity

A paper by Cox et al. introduces Ψ, “a theoretically informed metric of global temperature variability”, which scales with equilibrium climate sensitivity (ECS) across 16 General Circulation Models (GCMs). They report that Ψ provides a strong constraint on ECS, ruling out both high and low values. Our analysis shows that this constraint is sensitive to the GCMs considered, primarily reflects forced climate response rather than climate variability, and does not narrow uncertainty in ECS. It is therefore premature to rule out the possibility of large ECS values.

58 GEOSCIENCES↗
DOE OSTI2018

Human influence on the seasonal cycle of tropospheric temperature

We provide scientific evidence that a human-caused signal in the seasonal cycle of tropospheric temperature has emerged from the background noise of natural variability. Satellite data and the anthropogenic “fingerprint” predicted by climate models show common large-scale changes in geographical patterns of seasonal cycle amplitude. These common features include increases in amplitude at mid-latitudes in both hemispheres, amplitude decreases at high latitudes in the Southern Hemisphere, and small changes in the tropics. Simple physical mechanisms explain these features. The model fingerprint of seasonal cycle changes is identifiable with high statistical confidence in five out of six satellite temperature datasets. Our results suggest that attribution studies with the changing seasonal cycle provide powerful evidence for a significant human effect on Earth’s climate.

Santer, Benjamin D.↗
DOE OSTI2018

Sources of Intermodel Spread in the Lapse Rate and Water Vapor Feedbacks

Abstract Sources of intermodel differences in the global lapse rate (LR) and water vapor (WV) feedbacks are assessed using CO2 forcing simulations from 28 general circulation models. Tropical surface warming leads to significant warming and moistening in the tropical and extratropical upper troposphere, signifying a nonlocal, tropical influence on extratropical radiation and feedbacks. Model spread in the locally defined LR and WV feedbacks is pronounced in the Southern Ocean because of large-scale ocean upwelling, which reduces surface warming and decouples the surface from the tropospheric response. The magnitude of local extratropical feedbacks across models and over time is well characterized using the ratio of tropical to extratropical surface warming. It is shown that model differences in locally defined LR and WV feedbacks, particularly over the southern extratropics, drive model variability in the global feedbacks. The cross-model correlation between the global LR and WV feedbacks therefore does not arise from their covariation in the tropics, but rather from the pattern of warming exerting a common control on extratropical feedback responses. Because local feedbacks over the Southern Hemisphere are an important contributor to the global feedback, the partitioning of surface warming between the tropics and the southern extratropics is a key determinant of the spread in the global LR and WV feedbacks. It is also shown that model Antarctic sea ice climatology influences sea ice area changes and southern extratropical surface warming. As a result, model discrepancies in climatological Antarctic sea ice area have a significant impact on the intermodel spread of the global LR and WV feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Tropospheric Warming Over The Past Two Decades

Satellite temperature measurements do not support the recent claim of a “leveling off of warming” over the past two decades. Tropospheric warming trends over recent 20-year periods are always significantly larger (at the 10% level or better) than model estimates of 20-year trends arising from natural internal variability. Over the full 38-year period of the satellite record, the separation between observed warming and internal variability estimates is even clearer. In two out of three recent satellite datasets, the tropospheric warming from 1979 to 2016 is unprecedented relative to internally generated temperature trends on the 38-year timescale.

54 ENVIRONMENTAL SCIENCES↗