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Rising temperatures, rising risks

Climate change brings with it the increasing risk of extinction across species and systems. Marine species face particular risks related to water warming and oxygen depletion. Penn and Deutsch looked at extinction risk for marine species across climate warming and as related to ecophysiological limits (see the Perspective by Pinsky and Fredston). They found that under business-as-usual global temperature increases, marine systems are likely to experience mass extinctions on par with past great extinctions based on ecophysiological limits alone. Drastically reducing global emissions, however, offers substantial protection, which emphasizes a need for rapid action to prevent possibly catastrophic marine extinctions. —SNV

Abstract

Global warming threatens marine biota with losses of unknown severity. Here, we quantify global and local extinction risks in the ocean across a range of climate futures on the basis of the ecophysiological limits of diverse animal species and calibration against the fossil record. With accelerating greenhouse gas emissions, species losses from warming and oxygen depletion alone become comparable to current direct human impacts within a century and culminate in a mass extinction rivaling those in Earth’s past. Polar species are at highest risk of extinction, but local biological richness declines more in the tropics. Reversing greenhouse gas emissions trends would diminish extinction risks by more than 70%, preserving marine biodiversity accumulated over the past ~50 million years of evolutionary history.

So let's assume we need 2 ha/person or more agricultural land post-fossil. Soil loss/aridification/chaotic climate could mean a lot more than that.

Abstract

The 2015 Paris agreement was established to limit Greenhouse gas (GHG) global warming below 1.5°C above preindustrial era values. Knowledge of climate sensitivity to GHG levels is central for formulating effective climate policies, yet its exact value is shroud in uncertainty. Climate sensitivity is quantitatively expressed in terms of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR), estimating global temperature responses after an abrupt or transient doubling of CO2. Here, we represent the complex and highly-dimensional behavior of modelled climate via low-dimensional emergent networks to evaluate Climate Sensitivity (netCS), by first reconstructing meaningful components describing regional subprocesses, and secondly inferring the causal links between these to construct causal networks. We apply this methodology to Sea Surface Temperature (SST) simulations and investigate two different metrics in order to derive weighted estimates that yield likely ranges of ECS (2.35–4.81°C) and TCR (1.53-2.60°C). These ranges are narrower than the unconstrained distributions and consistent with the ranges of the IPCC AR6 estimates. More importantly, netCS demonstrates that SST patterns (at “fast” timescales) are linked to climate sensitivity; SST patterns over the historical period exclude median sensitivity but not low-sensitivity (ECS < 3.0°C) or very high sensitivity (ECS ≥ 4.5°C) models.

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.