Predictability of weather and climate

Abstract:

Abstract Record-breaking extreme rainfall events, such as those observed in Southeast Africa (SEAF) in February 2023, underscore the urgent need to quantify anthropogenic impacts on extreme weather risks. This study presents an attribution and projection analysis of February extreme rainfall in SEAF by evaluating probabilistic risk changes across multiple Earth System Model (ESM) ensembles and Numerical Weather Prediction (NWP) experiments. While NWP models reliably capture the magnitude and frequency of these extremes, ESMs generally exhibit a wet bias, necessitating the use of relative thresholds. Our attribution analysis reveals that greenhouse gas (GHG) forcing significantly increases the risk of extreme events (e.g., once-in-70-year and once-in-150-year events, comparable to the precipitation intensities of 2000 and 2023). However, this risk increase remains less certain under all-forcing simulations, as ESMs show substantial disagreement regarding aerosol (AER) influences. Future projections under various Shared Socioeconomic Pathways (SSPs) indicate that a higher GHG emission exacerbates these risks, highlighting the critical importance of aggressive mitigation efforts. Furthermore, shifts in Generalized Extreme Value (GEV) parameters demonstrate how forcing-driven climate changes may deform the risk probability distribution, with a higher emission of GHG potentially altering both the climatological mean and interannual variability of SEAF extreme rainfall. Our findings are supported by both ESM and NWP results (particularly short-lead forecasts), with the latter exhibiting lower uncertainty due to constraints on synoptic systems and internal oceanic variability. We conclude that GHG forcing likely elevates the risk of 2023-like extreme events in SEAF and discuss the remaining sources of uncertainty in these projections.

Abstract:

We propose a new temporal merging method for seamless climate forecast information of climate extremes. In order to construct stable and seamless climate forecast information for user-oriented climate extremes, ensemble members are pooled across available start dates using relative weights, for a target season. Recently, vineyard farmers from Catalonia, Spain, reported frost risk during the spring months (March and April). The observations show high interannual variability of the frequency of frost days (FFD) over Catalonia for the period 1983–2022. Multi-annual to seasonal forecasts from the European Centre for Medium-Range Weather Forecasts show a varying level of FFD forecast skill for lead times 1 to 22 months. The temporally merged weighted forecast enhances forecast skill compared to individual start dates, by adding information from previous start dates, at no additional computational cost. For most start dates, the skill is primarily attributed to the warming trends, except for a few start dates, where remanent forecast skill is attributed to internal variability. The proposed anticipatory framework “Ready-Steady-Action/Go” shows that adopting seamless climate forecast information can bring about 20 to 30% potential economic value for vineyard farmers, with a maximum value noted for multi-annual forecasts.

Abstract:

Supporting stakeholders with science-based decision-making to mitigate and adapt to climate change impacts is a central mandate of the climate research community. In particular, mapping out scenario-dependent climate risk landscapes is one of the most pressing challenges. Increasingly, communities and regions are experiencing high-impact climate and weather extremes that arise from a complex interplay of processes and events acting across various spatial and temporal scales. To account for these emerging trends, there is a growing recognition that both climate impact and early warning research needs to incorporate risks from compound events to better inform climate adaptation and mitigation efforts. This demand for more fine-grained and applicable knowledge gives rise to new data and modeling needs, and can increase uncertainties. Consequently, new methodological approaches and effective communication strategies are required for making research usable outside scientific communities. In this perspective, we reflect on this usability challenge by discussing impact data products, early warning and modeling capabilities, and communication tools, urging climate impact scientists to increasingly incorporate usability considerations in their research to meet the pressing demand for usable compound event research.

Abstract:

Rational Quantum Mechanics (RaQM) is a theory consistent with Gerard ’t Hooft’s proposals about the locally causal nature of quantum physics. Based on a specific discretization of the Riemann Sphere, the quantum state only is only defined in bases where squared amplitudes and complex phases (divided by [Formula: see text]) are rational numbers. These “rational bases” correspond to ’t Hooft’s ontological bases in which the quantum state has a beable representation. Bell’s inequality is violated without breaking realism, locality or free choice. Instead, for each run in a Bell experiment, a number-theoretic property of the cosine function ensures that the squared amplitude of the singlet state is an irrational number in at least one of the two counterfactual pairs of bases needed to derive Bell’s inequality. Hence, the Measurement Independence (MI) assumption is violated — not for the nominal settings under the experimenters’ free control, but for the exact settings which were never under their control anyway. None of the grotesque conspiracies that plague conventional superdeterministic explanations of Bell’s Theorem applies. Bell’s Theorem implies RaQM is a holistic theory. As such, implications for models of cosmology are discussed. An experimental test of RaQM, achievable in about 5 years, is described.

Abstract:

The Earth’s middle atmosphere spans the deep region from the upper troposphere/lower stratosphere at around 10 km altitude to the mesosphere/lower thermosphere at around 100 km altitude. It is being increasingly recognized for its role in driving extreme surface weather and regional climate change. Climate models predict large ongoing and future changes in the middle atmosphere composition and circulation. However, the observations needed to detect, attribute and understand these changes and their impacts, to test predictions, and thereby to improve our models, are lacking. Here we show the capacity of infrared limb-imaging tomography to provide the needed observations. This evaluation is based on studies performed within a recent satellite mission concept – the Changing-Atmosphere Infrared Tomography Explorer, CAIRT. Observing thermal infrared emissions simultaneously from the middle troposphere at about 4 km up to the lower thermosphere at about 115 km altitude this technique provides observations of temperature and an extensive range of trace gases with unprecedented spatial resolution of about 50 by 50 km horizontally and about 1 km vertically. We show how these observations would (a) help to quantify the changing atmospheric circulation, (b) allow characterization and quantification of the gravity waves that are critical in driving this circulation, (c) reveal how variability in solar radiation and energetic particles propagate downward to affect regional climate at the surface, (d) detect how volcanic eruptions and wildfires impact the middle atmosphere and climate, and (e) resolve how stratosphere-troposphere exchange affects ozone and water vapor in the crucial and climate-relevant tropopause region.