SearcharxivSearch

arXiv · 2210.03245

21st Century Global and Regional Surface Temperature Projections

Abstract

Many regions across the globe broke their surface temperature records in recent years, further sparking concerns about the impending arrival of "tipping points" later in the 21st century. This study analyzes observed global surface temperature trends in three target latitudinal regions: the Arctic Circle, the Tropics, and the Antarctic Circle. We show that global warming is accelerating unevenly across the planet, with the Arctic warming at approximately three times the average rate of our world. We further analyzed the reliability of latitude-dependent surface temperature simulations from a suite of Coupled Model Intercomparison Project Phase 6 models and their multi-model mean. We found that GISS-E2-1-G and FGOALS-g3 were the best-performing models based on their statistical abilities to reproduce observational, latitude-dependent data. Surface temperatures were projected from ensemble simulations of the Shared Socioeconomic Pathway 2-4.5 (SSP2-4.5). We estimate when the climate will warm by 1.5, 2.0, and 2.5 degrees C relative to the preindustrial period, globally and regionally. GISS-E2-1-G projects that global surface temperature anomalies would reach 1.5, 2.0, and 2.5 degrees C in 2024 (+/-1.34), 2039 (+/-2.83), and 2057 (+/-5.03) respectively, while FGOALS-g3 predicts these "tipping points" would arrive in 2024 (+/-2.50), 2054 (+/-7.90), and 2087 (+/-10.55) respectively. Our results reaffirm a dramatic, upward trend in projected climate warming acceleration, with upward concavity in 21st century projections of the Arctic, which could lead to catastrophic consequences across the Earth. Further studies are necessary to determine the most efficient solutions to reduce global warming acceleration and maintain a low SSP, both globally and regionally.

Explore related subjects

Keep this discovery

BibTeXRIS

Nicole Ma, Jonathan H. Jiang, Kennard Hou, Yun Lin, Trung Vu, Philip E. Rosen, Yu Gu, Kristen A. Fahy. 2022-10-06. 21st Century Global and Regional Surface Temperature Projections. https://doi.org/10.1029/2022ea002662

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