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[Formula: see text] 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.

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 CO2 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 (S25 - 50). To date, however, S25 - 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 S25 - 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 S25 - 50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S25 - 50 signal and noise patterns are accompanied by large cooling of S25 - 50 (1 to 2[Formula: see text]C over 1986 to 2022) and low S25 - 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.

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.

Natural variability contributes to model-satellite differences in tropical tropospheric warming.

A long-standing discrepancy exists between general circulation models (GCMs) and satellite observations: The multimodel mean temperature of the midtroposphere (TMT) in the tropics warms at approximately twice the rate of observations. Using a large ensemble of simulations from a single climate model, we find that tropical TMT trends (1979-2018) vary widely and that a subset of realizations are within the range of satellite observations. Realizations with relatively small tropical TMT trends are accompanied by subdued sea-surface warming in the tropical central and eastern Pacific. Observed changes in sea-surface temperature have a similar pattern, implying that the observed tropical TMT trend has been reduced by multidecadal variability. We also assess the latest generation of GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). CMIP6 simulations with muted warming over the central and eastern Pacific also show reduced tropical tropospheric warming. We find that 13% of the model realizations have tropical TMT trends within the observed trend range. These simulations are from models with both small and large climate sensitivity values, illustrating that the magnitude of tropical tropospheric warming is not solely a function of climate sensitivity. For global averages, one-quarter of model simulations exhibit TMT trends in accord with observations. Our results indicate that even on 40-y timescales, natural climate variability is important to consider when comparing observed and simulated tropospheric warming and is sufficiently large to explain TMT trend differences between models and satellite data.

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.

Distance to care, enrollment and loss to follow-up of HIV patients during decentralization of antiretroviral therapy in Neno District, Malawi: A retrospective cohort study.

HIV/AIDS remains the second most common cause of death in low and middle-income countries (LMICs), and only 34% of eligible patients in Africa received antiretroviral therapy (ART) in 2013. This study investigated the impact of ART decentralization on patient enrollment and retention in rural Malawi. We reviewed electronic medical records of patients registered in the Neno District ART program from August 1, 2006, when ART first became available, through December 31, 2013. We used GPS data to calculate patient-level distance to care, and examined number of annual ART visits and one-year lost to follow-up (LTFU) in HIV care. The number of ART patients in Neno increased from 48 to 3,949 over the decentralization period. Mean travel distance decreased from 7.3 km when ART was only available at the district hospital to 4.7 km when ART was decentralized to 12 primary health facilities. For patients who transferred from centralized care to nearer health facilities, mean travel distance decreased from 9.5 km to 4.7 km. Following a transfer, the proportion of patients achieving the clinic's recommended ≥4 annual visits increased from 89% to 99%. In Cox proportional hazards regression, patients living ≥8 km from a health facility had a greater hazard of being LTFU compared to patients <8 km from a facility (adjusted HR: 1.7; 95% CI: 1.5-1.9). ART decentralization in Neno District was associated with increased ART enrollment, decreased travel distance, and increased retention in care. Increasing access to ART by reducing travel distance is one strategy to achieve the ART coverage and viral suppression objectives of the 90-90-90 UNAIDS targets in rural impoverished areas.

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.