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Climate change poses a considerable challenge for coffee farming, due to increasing temperatures, worsening weather perturbations, and shifts in the quantity and timing of precipitation. Of the actions required for ensuring climate resilience for coffee, changing the crop itself is paramount, and this may have to include using alternative coffee crop species. In this study we use a multidisciplinary approach to elucidate the identity, distribution, and attributes, of two minor coffee crop species from East Africa: Coffea racemosa and C. zanguebariae . Using DNA sequencing and morphology, we elucidate their phylogenetic relationships and confirm that they represent two distinct but closely related species. Climate profiling is used to understand their basic climatic requirements, which are compared to those of Arabica ( C. arabica ) and robusta ( C. canephora ) coffee. Basic agronomic data (including yield) and sensory information are provided and evaluated. Coffea racemosa and C. zanguebariae possess useful traits for coffee crop plant development, particularly heat tolerance, low precipitation requirement, high precipitation seasonality (dry season tolerance) and rapid fruit development (c. 4 months flowering to mature fruit). These attributes would be best accessed via breeding programs, although these species also have niche-market potential, particularly after further pre-farm selection and post-harvest optimization.

Tabular data, spreadsheets organized in rows and columns, are ubiquitous across scientific fields, from biomedicine to particle physics to economics and climate science 1 , 2 . The fundamental prediction task of filling in missing values of a label column based on the rest of the columns is essential for various applications as diverse as biomedical risk models, drug discovery and materials science. Although deep learning has revolutionized learning from raw data and led to numerous high-profile success stories 3 , 4 – 5 , gradient-boosted decision trees 6 , 7 , 8 – 9 have dominated tabular data for the past 20 years. Here we present the Tabular Prior-data Fitted Network (TabPFN), a tabular foundation model that outperforms all previous methods on datasets with up to 10,000 samples by a wide margin, using substantially less training time. In 2.8 s, TabPFN outperforms an ensemble of the strongest baselines tuned for 4 h in a classification setting. As a generative transformer-based foundation model, this model also allows fine-tuning, data generation, density estimation and learning reusable embeddings. TabPFN is a learning algorithm that is itself learned across millions of synthetic datasets, demonstrating the power of this approach for algorithm development. By improving modelling abilities across diverse fields, TabPFN has the potential to accelerate scientific discovery and enhance important decision-making in various domains. Tabular Prior-data Fitted Network, a tabular foundation model, provides accurate predictions on small data and outperforms all previous methods on datasets with up to 10,000 samples by a wide margin.

Corals from the northern Red Sea and Gulf of Aqaba exhibit extreme thermal tolerance. To examine the underlying gene expression dynamics, we exposed Stylophora pistillata from the Gulf of Aqaba to short-term (hours) and long-term (weeks) heat stress with peak seawater temperatures ranging from their maximum monthly mean of 27 °C (baseline) to 29.5 °C, 32 °C, and 34.5 °C. Corals were sampled at the end of the heat stress as well as after a recovery period at baseline temperature. Changes in coral host and symbiotic algal gene expression were determined via RNA-sequencing (RNA-Seq). Shifts in coral microbiome composition were detected by complementary DNA (cDNA)-based 16S ribosomal RNA (rRNA) gene sequencing. In all experiments up to 32 °C, RNA-Seq revealed fast and pervasive changes in gene expression, primarily in the coral host, followed by a return to baseline gene expression for the majority of coral (>94%) and algal (>71%) genes during recovery. At 34.5 °C, large differences in gene expression were observed with minimal recovery, high coral mortality, and a microbiome dominated by opportunistic bacteria (including Vibrio species), indicating that a lethal temperature threshold had been crossed. Our results show that the S. pistillata holobiont can mount a rapid and pervasive gene expression response contingent on the amplitude and duration of the thermal stress. We propose that the transcriptomic resilience and transcriptomic acclimation observed are key to the extraordinary thermal tolerance of this holobiont and, by inference, of other northern Red Sea coral holobionts, up to seawater temperatures of at least 32 °C, that is, 5 °C above their current maximum monthly mean

As the poleward-flowing western boundary current of the North Atlantic ocean, the Gulf Stream plays a key role in the climate system. Here we show that from 2001 to 2023, the Gulf Stream west of 68° W has experienced both surface-intensified warming due to heat uptake at a rate exceeding the global average and a bulk lateral shift towards its cooler shoreward side at a rate of about 6 ± 3 km per decade. The Gulf Stream west of 68° W now has an O(10)-m-thick surface layer of warmer (by ~ 1 °C) and lighter (by ~ 0.3 kg m−3) water, contributing to increased upper ocean stratification. Our results rely on over 25,000 temperature and salinity profiles collected by autonomous profiling floats and underwater gliders in the region, allowing robust estimation of trends and clear attribution of observed changes to both ocean heat uptake and a lateral shift of the Gulf Stream.Autonomous sampling enables increased data collection in the ocean to understand circulation and water property changes. This study uses data from underwater gliders and profiling floats to show a shoreward lateral shift in Gulf Stream waters, which have warmed and become lighter since 2001.

Episodes of heat waves combined with drought can have a devastating impact on agricultural production worldwide. These conditions, as well as many other types of stress combinations, impose unique physiological and developmental demands on plants and require the activation of dedicated pathways. Here, we review recent RNA sequencing studies of stress combination in plants, and conduct a meta-analysis of the transcriptome response of plants to different types of stress combination. Our analysis reveals that each different stress combination is accompanied by its own set of stress combination-specific transcripts, and that the response of different transcription factor families is unique to each stress combination. The alarming rate of increase in global temperatures, coupled with the predicted increase in future episodes of extreme weather, highlight an urgent need to develop crop plants with enhanced tolerance to stress combination. The uniqueness and complexity of the physiological and molecular response of plants to each different stress combination, highlighted here, demonstrate the daunting challenge we face in accomplishing this goal. Dedicated efforts combining field experimentation, omics, and network analyses, coupled with advanced phenotyping and breeding methods, will be needed to address specific crops and particular stress combinations relevant to maintaining our future food chain secured.

Pearl millet is an important cereal crop worldwide and shows superior heat tolerance. Here, we developed a graph-based pan-genome by assembling ten chromosomal genomes with one existing assembly adapted to different climates worldwide and captured 424,085 genomic structural variations (SVs). Comparative genomics and transcriptomics analyses revealed the expansion of the RWP-RK transcription factor family and the involvement of endoplasmic reticulum (ER)-related genes in heat tolerance. The overexpression of one RWP-RK gene led to enhanced plant heat tolerance and transactivated ER-related genes quickly, supporting the important roles of RWP-RK transcription factors and ER system in heat tolerance. Furthermore, we found that some SVs affected the gene expression associated with heat tolerance and SVs surrounding ER-related genes shaped adaptation to heat tolerance during domestication in the population. Our study provides a comprehensive genomic resource revealing insights into heat tolerance and laying a foundation for generating more robust crops under the changing climate.

Accurate knowledge of the location and magnitude of ocean heat content (OHC) variability and change is essential for understanding the processes that govern decadal variations in surface temperature, quantifying changes in the planetary energy budget, and developing constraints on the transient climate response to external forcings. We present an overview of the temporal and spatial characteristics of OHC variability and change as represented by an ensemble of dynamical and statistical ocean reanalyses (ORAs). Spatial maps of the 0–300 m layer show large regions of the Pacific and Indian Oceans where the interannual variability of the ensemble mean exceeds ensemble spread, indicating that OHC variations are well-constrained by the available observations over the period 1993–2009. At deeper levels, the ORAs are less well-constrained by observations with the largest differences across the ensemble mostly associated with areas of high eddy kinetic energy, such as the Southern Ocean and boundary current regions. Spatial patterns of OHC change for the period 1997–2009 show good agreement in the upper 300 m and are characterized by a strong dipole pattern in the Pacific Ocean. There is less agreement in the patterns of change at deeper levels, potentially linked to differences in the representation of ocean dynamics, such as water mass formation processes. However, the Atlantic and Southern Oceans are regions in which many ORAs show widespread warming below 700 m over the period 1997–2009. Annual time series of global and hemispheric OHC change for 0–700 m show the largest spread for the data sparse Southern Hemisphere and a number of ORAs seem to be subject to large initialization ‘shock’ over the first few years. In agreement with previous studies, a number of ORAs exhibit enhanced ocean heat uptake below 300 and 700 m during the mid-1990s or early 2000s. The ORA ensemble mean (±1 standard deviation) of rolling 5-year trends in full-depth OHC shows a relatively steady heat uptake of approximately 0.9 ± 0.8 W m −2 (expressed relative to Earth’s surface area) between 1995 and 2002, which reduces to about 0.2 ± 0.6 W m −2 between 2004 and 2006, in qualitative agreement with recent analysis of Earth’s energy imbalance. There is a marked reduction in the ensemble spread of OHC trends below 300 m as the Argo profiling float observations become available in the early 2000s. In general, we suggest that ORAs should be treated with caution when employed to understand past ocean warming trends—especially when considering the deeper ocean where there is little in the way of observational constraints. The current work emphasizes the need to better observe the deep ocean, both for providing observational constraints for future ocean state estimation efforts and also to develop improved models and data assimilation methods.

We use a version of the NOAA Climate Forecast System with enhanced (up to 1‐m) ocean model vertical resolution to investigate the mean diurnal cycles of upper ocean temperature and currents. The model sea surface temperature diurnal cycle agrees well with a global observational analysis. The simulated time‐depth profiles of temperature and current also match closely observations from densely instrumented moorings in the tropical Pacific. Our analyses provide new insights into subsurface ocean diurnal cycles. Significant temperature diurnal range occurs, with seasonal modulation, at depths greater than 10 m across broad areas of the subtropical and midlatitude oceans. Significant current diurnal cycles are evident below 30 m across parts of the tropics, including in areas where deep‐cycle turbulence has been observed. Plain Language Summary We used computer model simulations of Earth's atmosphere and ocean to understand how ocean temperatures and currents vary by time of day. The model has 12 levels in the top 20 m of the ocean—greater than normal for this kind of simulation. This allows realistic simulated diurnal variations of sea surface temperature (compared to global observations), and realistic changes in temperature and current at and below the surface (compared to mooring observations). These results give us confidence in the global simulations of currents, which provide new insights into diurnal variations of surface ocean velocity and turbulence below the surface. Key Points A 1‐m vertical resolution ocean model accurately simulates global patterns of sea surface temperature mean diurnal cycle The model gives new insights into modes of subsurface ocean temperature and current diurnal variation Global maps of current diurnal cycle extend understanding past that from relatively sparse observations

The Argo Program has been implemented and sustained for almost two decades, as a global array of about 4000 profiling floats. Argo provides continuous observations of ocean temperature and salinity versus pressure, from the sea surface to 2000 dbar. The successful installation of the Argo array and its innovative data management system arose opportunistically from the combination of great scientific need and technological innovation. Through the data system, Argo provides fundamental physical observations with broad societally-valuable applications, built on the cost-efficient and robust technologies of autonomous profiling floats. Following recent advances in platform and sensor technologies, even greater opportunity exists now than 20 years ago to (i) improve Argo’s global coverage and value beyond the original design, (ii) extend Argo to span the full ocean depth, (iii) add biogeochemical sensors for improved understanding of oceanic cycles of carbon, nutrients, and ecosystems, and (iv) consider experimental sensors that might be included in the future, for example to document the spatial and temporal patterns of ocean mixing. For Core Argo and each of these enhancements, the past, present, and future progression along a path from experimental deployments to regional pilot arrays to global implementation is described. The objective is to create a fully global, top-to-bottom, dynamically complete, and multidisciplinary Argo Program that will integrate seamlessly with satellite and with other in situ elements of the Global Ocean Observing System (Legler et al., 2015). The integrated system will deliver operational reanalysis and forecasting capability, and assessment of the state and variability of the climate system with respect to physical, biogeochemical, and ecosystems parameters. It will enable basic research of unprecedented breadth and magnitude, and a wealth of ocean-education and outreach opportunities.