Climate change increasing likelihood of extreme snowfall in the French Alps, research suggests
Global warming is often cited as having a negative impact on snow and ice melt in cold regions, yet new research published in The Cryosphere has suggested that extreme snowfall events may be a feature of some locations at higher latitudes and elevations in the decades to come.
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The scientists studied 23 massifs across the French Alps, inputting real data from ground measurements of temperature and daily precipitation and meteorological forecasts from 1951 to the present day in order to predict changes over the remainder of this century under +1°C of global warming.
They found that daily mean annual snowfall increased considerably at elevations above 3,600 m but decreased below 3,000 m, instead experiencing more precipitation, while over 100 years the average elevations decrease for both situations with increased snowfall above 3,300 m but reduced below 2,400 m.
In summary, this means the threshold at which the net balance of snowfall shifts above 0% changes from 3,000 m at +1.5°C warming to 3,300 m at +4°C for the annual mean, an elevation increase of 123 m per +1°C of warming, the steepest increase of which occurs above +3°C.
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Climate attribution method overstates “fingerprints” of external forcing
I have a new paper in the peer-reviewed journal Environmetrics discussing biases in the “optimal fingerprinting” method which climate scientists use to attribute climatic changes to greenhouse gas emissions. This is the third in my series of papers on flaws in standard fingerprinting methods: blog posts on the first two are here and here.
Climatologists use a statistical technique called Total Least Squares (TLS), also called orthogonal regression, in their fingerprinting models to fix a problem in ordinary regression methods that can lead to the influence of external forcings being understated. My new paper argues that in typical fingerprinting settings TLS overcorrects and imparts large upward biases, thus overstating the impact of GHG forcing.
While the topic touches on climatology, for the most part the details involve regression methods which is what empirical economists like me are trained to do. I teach regression in my econometrics courses and I have studied and used it all my career. I mention this because if anyone objects that I’m not a “climate scientist” my response is: you’re right, I’m an economist which is why I’m qualified to talk about this.
I have previously shown that when the optimal fingerprinting regression is misspecified by leaving out explanatory variables that should be in it, TLS is biased upwards (other authors have also proven this theoretically). In that study I noted that when anthropogenic and natural forcings (ANTH and NAT) are negatively correlated the positive TLS bias increases. My new paper focuses just on this issue since, in practice, climate model-generated ANTH and NAT forcing series are negatively correlated. I show that in this case, even if no explanatory variables have been omitted from the regression, TLS estimates of forcing coefficients are usually too large. Among other things, since TLS-estimated coefficients are plugged into carbon budget models, this will result in a carbon budget being biased too small.
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Video mit Willie Soon:
Global Societal Crises of the 17th Century: the Sun-Earth Connection with Professor Willie Soon
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The March 1940 Superstorm: Geoelectromagnetic Hazards and Impacts on American Communication and Power Systems
An analysis is made of geophysical records of the 24 March 1940, magnetic storm and related reports of interference on long-line communication and power systems across the contiguous United States and, to a lesser extent, Canada. Most long-line system interference occurred during local daytime, after the second of two storm sudden commencements and during the early part of the storm’s main phase. The high degree of system interference experienced during this storm is inferred to have been due to unusually large-amplitude and unusually rapid geomagnetic field variation, possibly driven by interacting interplanetary coronal-mass ejections. Geomagnetic field variation, in turn, induced geoelectric fields in the electrically conducting solid Earth, establishing large potential differences (voltages) between grounding points at communication depots and transformer substations connected by long transmission lines. It is shown that March 1940 storm-time communication- and power-system interference was primarily experienced over regions of high electromagnetic surface impedance, mainly in the upper Midwest and eastern United States. Potential differences measured on several grounded long lines during the storm exceeded 1-min resolution voltages that would have been induced by the March 1989 storm. In some places, voltages exceeded American electric-power-industry benchmarks. It is concluded that the March 1940 magnetic storm was unusually effective at inducing geoelectric fields. Although modern communication systems are now much less dependent on long electrically conducting transmission lines, modern electric-power-transmission systems are more dependent on such lines, and they, thus, might experience interference with the future occurrence of a storm as effective as that of March 1940.
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Climatic and non-climatic factors found to affect vegetation greenness in Sudano-Sahelian region of Africa
Fully understanding vegetation dynamics and potential drivers in the Sudano-Sahelian region of Africa is crucial for a better understanding of long-term changes and sustainable management of dryland ecosystems.
A research team from the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences (CAS) has analyzed trends in vegetation greenness at the pixel scale using time series of satellite data in the Sudano-Sahelian region during 2001–2020 and quantified the relative contributions of climatic factors and non-climatic factors in specific sub-regions. The study was published in Regional Environmental Change on June 28.
Using MODIS Normalized Difference Vegetation Index (NDVI) as a proxy for vegetation greenness, the researchers found that greening was widespread across the Sudano-Sahelian region, while browning was clustered in central West Africa.
They applied a correlation-based conceptual attribution model to study rainfall-driven changes. Results showed that only nearly half of the areas with vegetation greening could be explained by long-term rainfall variability, while most of the areas with browning trends were not related to rainfall variability. Greening/browning vegetation trends not caused by rainfall variability could be explained by the non-climatic factors, e.g., land use/land cover (LULC) change and fire impact.
LULC changes have significant local effects on vegetation greenness in specific sub-regions of the Sudano-Sahelian. By analyzing the fractional abundance of LULC classes within each NDVI pixel, the research team found that gains (i.e., increases in the fractional abundance of LULC classes) in cropland and natural vegetation associated with positive land management were likely the dominant drivers of greening in Senegal and Ethiopia. The combined impacts of rainfall variability and LULC changes contributed to greening trends in Mali and Sudan.
In contrast, vegetation browning in central West Africa appeared to be driven by cropland gain and natural vegetation loss (i.e., decrease in the fractional abundance of a LULC class) associated with extensive agricultural production activities.
Paper: Yelong Zeng et al, Changes in vegetation greenness related to climatic and non-climatic factors in the Sudano-Sahelian region, Regional Environmental Change (2023). DOI: 10.1007/s10113-023-02084-5