Machine learning for stochastic parametrization

Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the subgrid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterize uncertainty in small-scale processes. These techniques are now widely used across weather, subseasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrization schemes using machine learning (ML). This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments and discuss the potential for data-driven approaches for stochastic parametrization. We highlight early studies in this area and draw attention to the novel challenges that remain

The future of algal blooms in lakes globally is in our hands

Lakes are fundamental to society and nature, yet they are currently exposed to excessive nutrients and climate change, resulting in algal blooms. In the future, this may change, but how and where still needs more scientific attention. Here, we explore future trends in algal blooms in lakes globally for >3,500 ‘representative lakes’ for the year 2050, considering the attribution of both nutrient and climate factors. We soft-coupled a process-based lake ecosystem model (PCLake+) with a watershed nutrient model (MARINA-Multi) to assess trends in algal blooms in terms of the Trophic State Index for chlorophyll-a (TSI-Chla). Globally between 2010 and 2050, we show a rising trend in algal blooms under fossil-fuelled development (TSI-Chla increase in 91% of lakes) and a declining trend under sustainable development (TSI-Chla decrease in 63% of lakes). These changes are significantly attributed to nutrients. While not always significant, climate change attributions point to being unfavourable for lakes in 2050, exacerbating lake water quality. Our study stresses prioritising responsible nutrient and climate management on policy agendas. This implies that the future of algal blooms in lakes is in our hands

Zinc and copper have the greatest relative importance for river macroinvertebrate richness at a national scale

It is important to discover what change led to the improvement in European macroinvertebrate biodiversity in the period from 1990−2000s and what prevents further desirable gains from taking place today. A 30-year data set from 1,457 macroinvertebrate monitoring sites spread across England, with 65,032 discrete observations was combined with 41 chemical, physical, habitat, and geographic variables. This data set was analyzed using generalized linear mixed-effect models and generalized additive mixed models. To include all combinations of the variables required to address each question, required over 20,000 model runs. It was found that no variables were more consistently and strongly associated with the overall family richness than Zn and Cu. Zn and Cu led both for the era of large gains in richness up to 2005 and also in the later period of 2006–2018 when few further gains were made

Improving the reproducibility in geoscientific papers: lessons learned from a Hackathon in climate science

In this paper, we explore the crucial role and challenges of computational reproducibility in geosciences, drawing insights from the Climate Informatics Reproducibility Challenge (CICR) in 2023. The competition aimed at (1) identifying common hurdles to reproduce computational climate science; and (2) creating interactive reproducible publications for selected papers of the Environmental Data Science journal. Based on lessons learned from the challenge, we emphasize the significance of open research practices, mentorship, transparency guidelines, as well as the use of technologies such as executable research objects for the reproduction of geoscientific published research. We propose a supportive framework of tools and infrastructure for evaluating reproducibility in geoscientific publications, with a case study for the climate informatics community. While the recommendations focus on future CIRCs, we expect they would be beneficial for wider umbrella of reproducibility initiatives in geosciences

UK hydrological outlook - February 2025

The Hydrological Outlook provides an insight into future hydrological conditions across the UK. Specifically, it describes likely trajectories for river flows and groundwater levels on a monthly basis, with a particular focus on the next three months. Well established monitoring programmes provide the current status of both river flows and groundwater levels at many sites across the UK, and data from these programmes provide the starting point for the Outlook. A number of techniques are used to project forwards from the current state and results from these are used to produce a summary that includes a highlights map

Shared environmental similarity between relatives influences heritability of reproductive timing in wild great tits

Intraspecific variation is necessary for evolutionary change and population resilience, but the extent to which it contributes to either depends on the causes of this variation. Understanding the causes of individual variation in traits involved with reproductive timing is important in the face of environmental change, especially in systems where reproduction must coincide with seasonal resource availability. However, separating the genetic and environmental causes of variation is not straightforward, and there has been limited consideration of how small-scale environmental effects might lead to similarity between individuals that occupy similar environments, potentially biasing estimates of genetic heritability. In ecological systems, environments are often complex in spatial structure, and it may therefore be important to account for similarities in the environments experienced by individuals within a population beyond considering spatial distances alone. Here, we construct multi-matrix quantitative genetic animal models using over 11,000 breeding records (spanning 35 generations) of individually-marked great tits (Parus major) and information about breeding proximity and habitat characteristics to quantify the drivers of variability in two key seasonal reproductive timing traits. We show that the environment experienced by related individuals explains around a fifth of the variation seen in reproductive timing, and accounting for this leads to decreased estimates of heritability. Our results thus demonstrate that environmental sharing between relatives can strongly affect estimates of heritability and therefore alter our expectations of the evolutionary response to selection

UK hydrological outlook - March 2025

The Hydrological Outlook provides an insight into future hydrological conditions across the UK. Specifically, it describes likely trajectories for river flows and groundwater levels on a monthly basis, with a particular focus on the next three months. Well established monitoring programmes provide the current status of both river flows and groundwater levels at many sites across the UK, and data from these programmes provide the starting point for the Outlook. A number of techniques are used to project forwards from the current state and results from these are used to produce a summary that includes a highlights map

HTAP3 fires: towards a multi-model, multi-pollutant study of fire impacts

Open biomass burning has major impacts globally and regionally on atmospheric composition. Fire emissions include particulate matter, tropospheric ozone precursors, and greenhouse gases, as well as persistent organic pollutants, mercury, and other metals. Fire frequency, intensity, duration, and location are changing as the climate warms, and modelling these fires and their impacts is becoming more and more critical to inform climate adaptation and mitigation, as well as land management. Indeed, the air pollution from fires can reverse the progress made by emission controls on industry and transportation. At the same time, nearly all aspects of fire modelling– such as emissions, plume injection height, long-range transport, and plume chemistry– are highly uncertain. This paper outlines a multi-model, multipollutant, multi-regional study to improve the understanding of the uncertainties and variability in fire atmospheric science, models, and fires’ impacts, in addition to providing quantitative estimates of the air pollution and radiative impacts of biomass burning. Coordinated under the auspices of the Task Force on Hemispheric Transport of Air Pollution, the international atmospheric modelling and fire science communities are working towards the common goal of improving global fire modelling and using this multi-model experiment to provide estimates of fire pollution for impact studies. This paper outlines the research needs, opportunities, and options for the fire-focused multi-model experiments and provides guidance for these modelling experiments, outputs, and analyses that are to be pursued over the next 3 to 5 years. The paper proposes a plan for delivering specific products at key points over this period to meet important milestones relevant to science and policy audiences

COSMOS-UK. Soil moisture: March 2025

The COSMOS-UK soil moisture status report provides an insight into the current soil moisture conditions across the UK as monitored by the COSMOS-UK network. The network comprises approximately 50 sites at which a cosmic ray neutron sensor is deployed to monitor soil moisture within a footprint of about 12 hectares. The report is comprised of: maps of end of month soil moisture both as volumetric water content and as a soil moisture index; a short description of current status; and selected time series graphs showing data from the last three years

Surveying the deep: A review of computer vision in the benthos

The analysis of image data for benthic biodiversity monitoring is now commonplace within the domain of marine ecology. Whilst advances in imaging technologies have allowed for the collection of vast quantities of data, the curation of this has traditionally been performed manually, resulting in a bottleneck whereby data is collected faster than it can be processed. Recent years have seen marine ecologists turn to the domain of computer vision to help automate this curation process. However, as the knowledge required to build such systems spans both domains, there is a high barrier to entry. To help reduce this barrier, this paper aims to provide an introduction to computer vision-based benthic biodiversity monitoring via a comprehensive literature review. To aid ecologists, key computer vision concepts are described and example use-cases highlighted. The major challenges inherent to benthic imagery for computer vision systems are explored, alongside a discussion of how current systems attempt to mitigate against these. To aid computer scientists wishing to enter the domain, an exploration of currently available open-source benthic datasets is also provided. Recommendations for future research are explored, including a move towards human-centric techniques, committing to ablation studies, reaching community agreement on open-source benchmarking datasets, and an increased use of innovative methods to allow for improved answering of key benthic ecology questions