Today, a major challenge for climate science is to overcome what is called the “usability gap” between the projections derived fromclimate models and the needs of the end-users. Regional Climate Models (RCMs) are expected to provide usable information concerning a variety of impacts and for a wide range of end-users. It is often assumed that the development of more accurate, more complex RCMs with higher spatial resolution should bring process understanding and better local projections, thus overcoming the usability gap. In this paper, I rather assume that the credibility of climate information should be pursued together with two other criteria of usability, which are salience and legitimacy. Based on the Swiss climate change scenarios, I study the attempts at meeting the needs of end-users and outline the trade-off modellers and users have to face with respect to the cascade of uncertainty. A conclusion of this paper is that the trade-off between salience and credibility sets the conditions under which RCMs can be deemed adequate for the purposes of addressing the needs of end-users and gearing the communication of the projections toward direct use and action.
The projection of precipitation changes and the year of surpassing a 1, 2, 3, 4, and 5 °C warming above pre-industrial levels in the Middle East – West Asia (MEWA) during 2026–2100 was conducted using dynamical downscaling of the Regional Climate Modeling version 4.7 (RegCM4.7) under Shared Socio-economic Pathways (SSPs) scenarios. Two significant changes in annual precipitation were identified compared to the baseline period of 1990–2014: a decrease in the Mediterranean Basin (MB) and an increase in the Persian Gulf- the Gulf of Oman -east of the Arabian Peninsula region (POA). The above patterns were also detected during the spring of 2026–2050. However, a decrease in precipitation is anticipated around the Persian Gulf (PG) during 2076–2100. The precipitation patterns exhibit a decrease in the MB and east of it up to Iran during the summer. In contrast, there is an increase in precipitation in the POA. During autumn, precipitation increases (decreases) around the POA (MB). During the winter, there is an increase (decrease) in the precipitation of POA (from the MB to Iran). In the SSP5-8.5 scenario, a 2 °C (3 °C) warming is expected by 2050 (2068), about two (four) decades earlier than SSP2-4.5. A 4 °C (5 °C) warming is expected by 2081 (2092) in SSP5-8.5, but postponed beyond 2100 in SSP2-4.5. Out of all studied cities, Tehran is projected to experience the greatest decrease in precipitation and the highest increase in temperature. Meanwhile, Abu Dhabi is expected to encounter the greatest precipitation increase and the lowest temperature rise.
Seasonal precipitation forecasting is vital for weather-sensitive sectors. Global Circulation Models (GCM) routinely produce ensemble Seasonal Climate Forecasts (SCFs) but suffer from issues like low forecast resolution and skills. To address these issues in this study, we introduce a post-processing method, Quantile Ensemble Bayesian Model Averaging (QEBMA). It utilises quantiles from a GCM ensemble forecast to create a pseudo-ensemble forecast. Through their reasonable linear relationships with observations, each pseudo-member connects a hurdle distribution with a point mass at zero for dry months and a gamma distribution for wet months. These distributions are mixed to construct a forecast probability distribution with their weights, proportional to the quantiles’ historical forecast performance. QEBMA is applied to three GCMs, including GloSea5 from the United Kingdom, ECMWF from Europe and ACCESS-S1 from Australia, for monthly precipitation forecasts in 32 locations across four climate zones in Australia. Leave-one-month-out cross-validation results illustrate that QEBMA enhances forecast skills compared to raw GCMs and other post-processing techniques, including quantile mapping and Extended Copula Post-Processing (ECPP), for forecast lead time of 0 to 2 months, based on five metrics. The skill improvements achieved by QEBMA are often statistically significant, particularly when compared to raw GCM forecasts across the 32 study locations. Among these post-processing models, only QEBMA consistently outperforms the SCF benchmark climatology, offering a promising alternative for improving seasonal precipitation forecasts.
This study aimed to identify the specific home environment factors that were judged to support or hinder the development of children’s critical reading skills. Using a Delphi method, 32 experts in Finland listed a set of home-related factors that can either hinder or support the development of children’s critical reading skills. The experts then evaluated and ranked the factors according to their perceived importance. A large set of home-related factors was produced. Out of these, we identified 13 supportive and nine hindering factors. The factors highlighted the importance of having a space for the child to be heard and involved in family discussions, having a space for differing viewpoints and critical thinking, parental competencies to support critical reading skills, and positive parental attitudes towards schooling and learning. The findings can be used for measurement and intervention development purposes.
This study aimed to identify the specific home environment factors that were judged to support or hinder the development of children’s critical reading skills. Using a Delphi method, 32 experts in Finland listed a set of home-related factors that can either hinder or support the development of children’s critical reading skills. The experts then evaluated and ranked the factors according to their perceived importance. A large set of home-related factors was produced. Out of these, we identified 13 supportive and nine hindering factors. The factors highlighted the importance of having a space for the child to be heard and involved in family discussions, having a space for differing viewpoints and critical thinking, parental competencies to support critical reading skills, and positive parental attitudes towards schooling and learning. The findings can be used for measurement and intervention development purposes.
Some have heralded generative AI models as an opportunity to inform diplomacy and support diplomats’ communication campaigns. Others have argued that generative AI is inherently untrustworthy because it simply manages probabilities and doesn’t consider the truth value of statements. In this article, we examine how AI applications are built to smooth over uncertainty by providing a single answer among multiple possible answers and by presenting information in a tone and form that demands authority. We contrast this with the practices of public diplomacy professionals who must grapple with both epistemic and aleatory uncertainty head on to effectively manage complexities through negotiation. We argue that the rise of generative AI and its “operationalization of truth” invites us to reflect on the possible shortcoming of AI’s application to public diplomacy practices and to recognize how prominent uncertainty is in public diplomacy practices.