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Why is the land drying out so fast? It's partly because there is more heat trapped in the atmosphere by greenhouse gases emitted from burning fossil fuels. This excess heat has exacerbated evaporation and is drawing more moisture out of soil.

Climate change has also made the weather more volatile. When drought does cede to rain, more of it arrives in bruising downpours that slough the topsoil

Rising temperatures, increasing precipitation, thawing permafrost and melting ice are pushing the Arctic outside its historical norms

Faster then expected ?

Abstract

Biodiversity is in rapid decline, but the extent of loss is not well resolved for poorly known groups. We estimate the number of extinctions for Australian non-marine invertebrates since the European colonisation of the continent. Our analyses use a range of approaches, incorporate stated uncertainties and recognise explicit caveats. We use plausible bounds for the number of species, two approaches for estimating extinction rate, and Monte Carlo simulations to select combinations of projected distributions from these variables. We conclude that 9,111 (plausible bounds of 1,465 to 56,828) Australian species have become extinct over this 236-year period. These estimates dwarf the number of formally recognised extinctions of Australian invertebrates (10 species) and of the single invertebrate species listed as extinct under Australian legislation. We predict that 39–148 species will become extinct in 2024. This is inconsistent with a recent pledge by the Australian government to prevent all extinctions. This high rate of loss is largely a consequence of pervasive taxonomic biases in community concern and conservation investment. Those characteristics also make it challenging to reduce that rate of loss, as there is uncertainty about which invertebrate species are at the most risk. We outline conservation responses to reduce the likelihood of further extinctions.

Abstract

The importance of climate change for driving adverse climate impacts has motivated substantial effort to understand the rate and magnitude of regional climate change in different parts of the world. However, despite decades of research, there is substantial uncertainty in the time remaining until specific regional temperature thresholds are reached, with climate models often disagreeing both on the warming that has occurred to-date, as well as the warming that might be experienced in the next few decades. Here, we adapt a recent machine learning approach to train a convolutional neural network to predict the time (and its uncertainty) until different regional warming thresholds are reached based on the current state of the climate system. In addition to predicting regional rather than global warming thresholds, we include a transfer learning step in which the climate-model-trained network is fine-tuned with limited observations, which further improves predictions of the real world. Using observed 2023 temperature anomalies to define the current climate state, our method yields a central estimate of 2040 or earlier for reaching the 1.5 °C threshold for all regions where transfer learning is possible, and a central estimate of 2040 or earlier for reaching the 2.0 °C threshold for 31 out of 34 regions. For 3.0 °C, 26 °C out of 34 regions are predicted to reach the threshold by 2060. Our results highlight the power of transfer learning as a tool to combine a suite of climate model projections with observations to produce constrained predictions of future temperatures based on the current climate.

cross-posted from: https://slrpnk.net/post/16014827

Three leading climate scientists have combined insights from 10 global climate models and, with the help of artificial intelligence (AI), conclude that regional warming thresholds are likely to be reached faster than previously estimated.

Mmmm faster then expected huh ?

Abstract

Recent studies project that temperature-related mortality will be the largest source of damage from climate change, with particular concern for the elderly whom it is believed bear the largest heat-related mortality risk. We study heat and mortality in Mexico, a country that exhibits a unique combination of universal mortality microdata and among the most extreme levels of humid heat. Combining detailed measurements of wet-bulb temperature with age-specific mortality data, we find that younger people who are particularly vulnerable to heat: People under 35 years old account for 75% of recent heat-related deaths and 87% of heat-related lost life years, while those 50 and older account for 96% of cold-related deaths and 80% of cold-related lost life years. We develop high-resolution projections of humid heat and associated mortality and find that under the end-of-century SSP 3–7.0 emissions scenario, temperature-related deaths shift from older to younger people. Deaths among under-35-year-olds increase 32% while decreasing by 33% among other age groups.

Not that great an essay, but has enough meat to post.

Highlights

Spatial precipitation patterns in Eastern China were developed over the Common Era.

Changes in precipitation patterns contribute to transitions of unified dynasties.

Positive phase superpositions of rainfall patterns align with prolonged droughts.

Abstract

Past studies have often associated the transitions of Chinese dynasties with nationwide climate change, overlooking significant spatial heterogeneity in precipitation anomalies across China. Historical changes in spatial precipitation patterns in response to Asian monsoon variability and their societal impact have not been fully explored. In this study, we present a new extended annual laminated speleothem record from central China covering the last 2000 years. By integrating paleoclimatic data from northern and southern China, we reconstructed the history of spatial precipitation patterns dominated by tripole and dipole patterns over the Common Era. Our analysis revealed that although the relationship between the monsoon and precipitation patterns was non-stationary, the positive phases of both patterns occurred more frequently during periods of monsoon weakening on multidecadal to multicentennial timescales. Moreover, the phase and intensity of these precipitation patterns varied across different intervals during the Chinese dynasties. Notably, transitions of unified dynasties often coincide with the simultaneous occurrence of the positive phases of both patterns on multidecadal timescales. This phase configuration of the patterns aligns with prolonged droughts in Eastern China, coinciding with historical records of reduced grain harvests and economic decline. Our findings highlight that historical changes in spatial configuration, rather than the nationwide synchronicity of precipitation anomalies, play a crucial role in Chinese dynastic transitions.

Abstract

In 2023, the global mean temperature soared to almost 1.5K above the pre-industrial level, surpassing the previous record by about 0.17K. Previous best-guess estimates of known drivers including anthropogenic warming and the El Niño onset fall short by about 0.2K in explaining the temperature rise. Utilizing satellite and reanalysis data, we identify a record-low planetary albedo as the primary factor bridging this gap. The decline is apparently caused largely by a reduced low-cloud cover in the northern mid-latitudes and tropics, in continuation of a multi-annual trend. Further exploring the low-cloud trend and understanding how much of it is due to internal variability, reduced aerosol concentrations, or a possibly emerging low-cloud feedback will be crucial for assessing the current and expected future warming.

Editor’s summary

Because it is clear that human activities are altering the global climate, researchers have been studying potential effects and predicting declines and extinctions. Understanding the consequences globally requires the synthesis of many studies. Following up on an initial effort nearly 10 years ago, Urban found that we can expect, with increased certainty, that rising temperatures will lead to an increasing number of extinctions, with the highest emission scenario leading to extinction of nearly a third of the Earth’s species, especially those from particular vulnerable taxa or regions. —Sacha Vignieri

Abstract

Climate change is expected to cause irreversible changes to biodiversity, but predicting those risks remains uncertain. I synthesized 485 studies and more than 5 million projections to produce a quantitative global assessment of climate change extinctions. With increased certainty, this meta-analysis suggests that extinctions will accelerate rapidly if global temperatures exceed 1.5°C. The highest-emission scenario would threaten approximately one-third of species, globally. Amphibians; species from mountain, island, and freshwater ecosystems; and species inhabiting South America, Australia, and New Zealand face the greatest threats. In line with predictions, climate change has contributed to an increasing proportion of observed global extinctions since 1970. Besides limiting greenhouse gases, pinpointing which species to protect first will be critical for preserving biodiversity until anthropogenic climate change is halted and reversed.

If fossil fuels keep burning at present rates, we are headed for apocalyptic civilisational collapse. Perhaps even more strangely, there is no longer much serious disagreement about this claim.

$200bn wave of new gas projects could lead to a “climate bomb” equivalent to releasing the annual emissions of all the world’s operating coal power plants, according to a report.

Highlights

First study on Daphnia chronic toxicity for PFAS and MP.

Daphnia genotypes with distinct histories of chemical exposure reveal the compounded effect of pollutants exposure.

PFAS and MP mixtures lead to developmental failures, delayed maturation, and reduced growtho Historical pollution exposure lowers tolerance to chemical mixtures.

The combined effect of the persistent chemicals analyses was 59% additive and 41% synergistic.

Abstract

Persistent chemicals from industrial processes, particularly perfluoroalkyl substances (PFAS), have become pervasive in the environment due to their persistence, long half-lives, and bioaccumulative properties. Used globally for their thermal resistance and repellence to water and oil, PFAS have led to widespread environmental contamination. These compounds pose significant health risks with exposure through food, water, and dermal contact. Aquatic wildlife is particularly vulnerable as water bodies act as major transport and transformation mediums for PFAS. Their co-occurrence with microplastics may intensify the impact on aquatic species by influencing PFAS sorption and transport. Despite progress in understanding the occurrence and fate of PFAS and microplastics in aquatic ecosystems, the toxicity of PFAS mixtures and their co-occurrence with other high-concern compounds remains poorly understood, especially over organisms’ life cycles.

Our study investigates the chronic toxicity of PFAS and microplastics on the sentinel species Daphnia, a species central to aquatic foodwebs and an ecotoxicology model. We examined the effects of perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), and polyethylene terephthalate microplastics (PET) both individually and in mixtures on Daphnia ecological endpoints. Unlike conventional studies, we used two Daphnia genotypes with distinct histories of chemical exposure. This approach revealed that PFAS and microplastics cause developmental failures, delayed sexual maturity and reduced somatic growth, with historical exposure to environmental pollution reducing tolerance to these persistent chemicals due to cumulative fitness costs. We also observed that the combined effect of the persistent chemicals analysed was 59% additive and 41% synergistic, whereas no antagonistic interactions were observed. The genotype-specific responses observed highlight the complex interplay between genetic background and pollutant exposure, emphasizing the importance of incorporating multiple genotypes in environmental risk assessments to more accurately predict the ecological impact of chemical pollutants.

Abstract

Snow is particularly impacted by climate change and therefore there is an urgent need to understand the temporal and spatial variability of depth of snowfall (HN) trends. However, the analysis of historical HN observations on large-scale areas is often impeded by lack of continuous long-term time series availability. This study investigates HN trends using observed time series spanning the period 1920–2020 from 46 sites in the Alps at different elevations. To discern patterns and variations in HN over the years, our analysis focuses also on key parameters such as precipitation (P), mean air temperature (TMEAN), and large-scale synoptic descriptors, that is, the North Atlantic Oscillation (NAO), Arctic Oscillation (AO) and Atlantic Multidecadal Oscillation (AMO) indices. Our findings reveal that in the last 100 years and below 2000 m a.s.l., despite a slight increase in winter precipitation, there was a decrease in HN over the Alps, especially for southern and low-elevation sites. The South-West and South-East regions experienced an average loss of 4.9 and 3.8%/decade, respectively. A smaller relative loss was found in the Northern region (2.3%/decade). The negative HN trends can be mainly explained by an increase of TMEAN by 0.15°C/decade. Most of the decrease in HN occurred mainly between 1980 and 2020, as a result of a more pronounced increase in TMEAN. This is also confirmed by the change of the running correlation between HN and TMEAN, NAO, AO over time, which until 1980 were not correlated at all, while the correlation increased in later years. This suggests that in more recent years favourable combinations of temperature, precipitation, and atmospheric pattern have become more crucial for snowfall to occur. On the other hand, no correlation was found with the AMO index.