Climate

This study analyzes coupled atmosphere–ocean variability in the South Atlantic Ocean. To do so, we characterize the spatio-temporal variability of annual mean sea-surface temperature (SST) and sea-level pressure (SLP) using Multichannel Singular Spectrum Analysis (M-SSA). We applied M-SSA to ERA5 reanalysis data (1959–2022) of South Atlantic SST and SLP, both individually and jointly, and identified a nonlinear trend, as well as two climate oscillations. The leading oscillation, with a period of 13 years, consists of a basin-wide southwest–northeast dipole and is observed both in the individual variables and in the coupled analysis. This mode is reminiscent of the already known South Atlantic Dipole, and it is probably related to the Pacific Decadal Oscillation and to El Niño–Southern Oscillation in the Pacific Ocean. The second oscillation has a 5-year period and also displays a dipolar structure. The main difference between the spatial structure of the decadal, 13-year, and the interannual, 5-year mode is that, in the first one, the SST cold tongue region in the southeast Atlantic’s Cape Basin is included in the pole closer to the equator. Together, these two oscillatory modes, along with the trend, capture almost 40% of the total interannual variability of the SST and SLP fields, and of their co-variability. These results provide further insights into the spatio-temporal evolution of SST and SLP variability in the South Atlantic, in particular as it relates to the South Atlantic Dipole and its predictability.

The rapid development of solar energy worldwide has attracted increasing attention due to its climatic and environmental impacts. Using MODIS data, we quantified the effects of solar farms (SFs) on albedo, vegetation (using enhanced vegetation index (EVI) as a proxy), and land surface temperature (LST) based on 116 large SFs across the world. The results show that the installation of SFs decreased the annual mean surface shortwave albedo by 0.016 ± 0.009 (mean ± 1 STD) and reduced the EVI by 0.015 ± 0.019 relative to the surrounding areas. SFs produced a strong cooling effect of −0.49 ± 0.43 K in the annual mean land surface temperature during the daytime and a weaker cooling effect of −0.21 ± 0.25 K during the nighttime. The greatest impacts on albedo and daytime LST were observed in barren land, followed by grassland and cropland, while the opposite order applied for vegetation impact. In terms of seasonal and latitudinal variations, the largest impact was observed at high latitudes in winter on albedo, at mid-latitudes in summer on vegetation, and at low latitudes in spring–summer transitions on daytime LST. Correlation analysis showed that the albedo and LST impacts were enhanced over large SFs with high capacity. The vegetation and LST impacts were both correlated with geographic and climatic factors and dependent on the type of SF (photovoltaic or concentrating solar power). Our global assessment provides observational evidence for the effects of SF construction on the environment and local climate, which can help the sustainable development of solar energy.

The development of wind energy is essential for decarbonizing energy production. However, the construction of wind farms changes land surface temperature (LST) and vegetation by modifying land surface properties and disturbing land–atmosphere interactions. In this study, we used moderate resolution imaging spectroradiometer satellite data to quantify the impacts on local climate and vegetation of 319 wind farms in the United States. Our results indicated insignificant impacts on LST during the daytime but significant warming of 0.10 \textdegree C of annual mean nighttime LST averaged over all wind farms, and 0.36 \textdegree C for those 61% wind farms with warming. The nighttime LST impacts exhibited seasonal variations, with stronger warming in winter and autumn, up to 0.18 \textdegree C, but weaker effects in summer and spring. We observed a decrease in peak normalized difference vegetation index (NDVI) for 59% of wind farms due to infrastructure construction, with an average reduction of 0.0067 compared to non-wind farm areas. The impacts of wind farms depended on wind farm size, with winter LST impacts for large and small wind farms ranging from 0.21 \textdegree C to 0.14 \textdegree C, and peak NDVI impacts ranging from -0.009 to -0.006. The LST impacts declined with the increasing distance from the wind farm, with detectable impacts up to 10 km. In contrast, the vegetation impacts on NDVI were only evident within the wind farm locations. Wind farms built in grassland and cropland showed larger warming effects but weaker vegetation impact than those built on forests. Furthermore, spatial correlation analyses with environmental factors suggest limited geographical controls on the heterogeneous wind farm impacts and highlight the important role of local factors. Our analyses based on a large sample offer new evidence for wind farm impacts with improved representativeness compared to previous studies. This knowledge is important to fully understand the climatic and environmental implications of energy system decarbonization.

Due to the physical coupling between atmosphere and ocean, information about the ocean helps to better predict the future of the atmosphere, and in turn, information about the atmosphere helps to better predict the ocean. Here, we investigate the spatial and temporal nature of this predictability: where, for how long, and at what frequencies does the ocean significantly improve prediction of the atmosphere, and vice versa? We apply Granger causality, a statistical test to measure whether a variable improves prediction of another, to local time series of sea surface temperature (SST) and low-level atmospheric variables. We calculate the detailed spatial structure of the atmosphere-to-ocean and ocean-to-atmosphere predictability. We find that the atmosphere improves prediction of the ocean most in the extratropics, especially in regions of large SST gradients. This atmosphere-to-ocean predictability is weaker but longer-lived in the tropics, where it can last for several months in some regions. On the other hand, the ocean improves prediction of the atmosphere most significantly in the tropics, where this predictability lasts for months to over a year. However, we find a robust signature of the ocean on the atmosphere almost everywhere in the extratropics, an influence that has been difficult to demonstrate with model studies. We find that both the atmosphere-to-ocean and ocean-to-atmosphere predictability are maximal at low frequencies, and both are larger in the summer hemisphere. The patterns we observe generally agree with dynamical understanding and the results of the Kalnay dynamical rule, which diagnoses the direction of forcing between the atmosphere and ocean by considering the local phase relationship between simultaneous sea surface temperature and vorticity anomaly signals. We discuss applications to coupled data assimilation.

Wind and solar farms offer a major pathway to clean, renewable energies. However, these farms would significantly change land surface properties, and, if sufficiently large, the farms may lead to unintended climate consequences. In this study, we used a climate model with dynamic vegetation to show that large-scale installations of wind and solar farms covering the Sahara lead to a local temperature increase and more than a twofold precipitation increase, especially in the Sahel, through increased surface friction and reduced albedo. The resulting increase in vegetation further enhances precipitation, creating a positive albedo–precipitation–vegetation feedback that contributes ~80% of the precipitation increase for wind farms. This local enhancement is scale dependent and is particular to the Sahara, with small impacts in other deserts.

Simple models of glacier volume evolution are important in understanding features of glacier response to climate change, due to the scarcity of data adequate for running more complex models on a global scale. Two quantities of interest in a glacier’s response to climate changes are its response time and its volume sensitivity to changes in the equilibrium line altitude (ELA). We derive a simplified, computationally inexpensive model of glacier volume evolution based on a block model with volume–area–length scaling. After analyzing its steady-state properties, we apply the model to each mountain glacier worldwide and estimate regionally differentiated response times and sensitivities to ELA changes. We use a statistical method from the family of global sensitivity analysis methods to determine the glacier quantities, geometric and climatic, that most influence the model output. The response time is dominated by the climatic setting reflected in the mass-balance gradient in the ablation zone, followed by the surface slope, while volume sensitivity is mainly affected by glacier size, followed by the surface slope.