Search arXivSearch

arXiv · 2406.05253

Contribution of Shorter-term Radiative Forcings of Aerosols and Ozone to Global Warming in the Last Two Decades

Abstract

This paper reports observations of regional and global upper stratosphere temperature (UST) and surface temperature, as well as various climate drivers including greenhouse gases (GHGs), ozone, aerosols, solar variability, snow cover extent, and sea ice extent (SIE). We strikingly found warming trends of 0.77(+/-0.57) and 0.69(+/-0.22) K/decade in UST at altitudes of 35-40 km in the Arctic and Antarctic respectively and no significant trends over non-polar regions since 2002. These UST trends provide fingerprints of decreasing and no significant trends in total GHG effect in polar and non-polar regions respectively. Correspondingly, we made the first observation of surface cooling trends in both the Antarctic since 2005 and the Arctic since 2016 once the SIE started to recover. But surface warming remains at mid-latitudes, which causes the recent rise in global mean surface temperature (GMST). These temperature changing patterns are consistent with the characteristics of the cosmic-ray-driven electron reaction (CRE) mechanism of halogen-containing GHGs (halo-GHGs) with larger destruction rates at higher latitudes. Moreover, the no-parameter physics model of warming caused by halo-GHGs reproduces closely the observed GMSTs from 2000 to 2024, including the almost no warming during 2000-2012 and the significant warming by 0.2-0.3 deg C during 2013-2023, of which 0.27 deg C was calculated to arise from the net radiative forcing of aerosols and ozone due to improved air quality. The results also show that the physics model captures 76% of the variance in the observed GMSTs, exhibiting a warming peak in October 2023 and predicting a gradual GMST reversal thereafter. The results from this study may greatly improve our understanding of global climate change and lead to the identifying of the correct major culprit for human contribution to changing the climate.

Explore related subjects

Keep this discovery

BibTeXRIS

Qing-Bin Lu. 2024-06-07. Contribution of Shorter-term Radiative Forcings of Aerosols and Ozone to Global Warming in the Last Two Decades. https://arxiv.org/abs/2406.05253

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Windowed Envelope Statistics for Time-Domain Significant Wave Height Estimation From HF Radar

Significant wave height (SWH) retrieval from high-frequency (HF) radar typically relies on a weak second-order Doppler continuum that is sensitive to noise, interference, and spectral leakage. This letter presents a Windowed Envelope Statistics Estimator (WESE) that operates directly on beam-formed time-domain voltages. A second-order term obtained from a Neumann expansion of the rough-surface field equation motivates quadratic compensation of localized radar features. WESE extracts the mean, standard deviation, or variance from overlapping windows of the in-phase, quadrature, or envelope-magnitude sequence, followed by quadratic compensation, rank ordering, least-squares regression, and causal smoothing. Evaluation used 335 synchronized hourly observations from a 13.385 MHz, 12-element WERA system at Argentia, Newfoundland and Labrador. The optimal configuration used quadrature variance, a 16-sample window, 896 retained chronological samples, and 30-h smoothing, achieving an RMSE of 0.152 m and a Pearson correlation of 0.978. This represents RMSE reductions of 32.1% and 18.7% relative to previously reported linear and second-order compensated ordered-statistics models, respectively. The results demonstrate robust time-domain SWH estimation without explicit Doppler-spectrum construction.

physics.ao-ph

KiloDA: Reconstructing kilometer-scale near-surface wind states from sparse station observations

Accurate kilometer-scale near-surface winds are important for understanding atmospheric processes over complex terrain, yet remain difficult to reconstruct from sparse and unevenly distributed observations. Here we introduce KiloDA, a diffusion framework for hourly kilometer-scale wind reconstruction from surface stations. KiloDA learns the statistical distribution and spatial structure of wind fields from historical 3-km Weather Research and Forecasting (WRF) model forecasts. At each reconstruction time, no contemporaneous WRF field is used. Instead, station observations provide the only constraints on the current atmospheric state and guide posterior sampling from the learned prior. In idealized WRF experiments, KiloDA recovers localized wind structures when only 0.24% of grid cells are observed and shows an overall advantage over conventional interpolation across terrain conditions and wind speed regimes. This capability largely transfers to real observations. In a fully withheld region, KiloDA reduces the median wind speed root mean square error (RMSE) by 19% relative to ERA5 reanalysis, using only observations outside the region, with the largest improvements over high-elevation and high-relief terrain. A random station holdout further confirms that this advantage extends across different complex-terrain locations and holdout configurations. These results show that historical model archives can provide useful structural knowledge for reconstructing kilometer-scale wind fields from sparse observations without requiring an accurate model estimate of the current atmospheric state.

physics.ao-ph