United in Science: Reboot climate action

State of climate science: the need for urgent and ambitious climate action

Human-caused climate change has resulted in widespread and rapid changes in the atmosphere, ocean, cryosphere and biosphere. The year 2023 was the warmest on record by a large margin, with widespread extreme weather. This trend continued in the first half of 2024.

Global greenhouse gas (GHG) emissions rose by 1.2% from 2021 to 2022, reaching 57.4 billion tons of carbon dioxide (CO2) equivalent. Globally averaged surface concentrations of CO2, methane (CH4) and nitrous oxide (N2O) also reached new highs.

When the Paris Agreement was adopted, greenhouse gas emissions were projected to increase by 16% by 2030 relative to 2015. Now, that projected increase is 3%, indicating progress has been made. Yet the emissions gap for 2030 remains high. To limit global warming to below 2 °C and 1.5 °C (above the pre-industrial era), global GHG emissions in 2030 must be reduced by 28% and 42%, respectively, from the levels projected from current policies.

With existing policies and Nationally Determined Contributions (which present national efforts to limit global warming to well below 2 °C), it is estimated that global warming will be kept to a maximum of 3 °C throughout the century. Only in the most optimistic scenario where all conditional NDCs and net-zero pledges are fully achieved, s global warming projected to be limited to 2 °C, with just a 14% chance of limiting global warming to 1.5 °C.

There is an 80% chance that the global mean near-surface temperature in at least one of the next five calendar years will exceed 1.5 °C above pre-industrial levels, and a 47% chance that the 2024-2028 five-year mean will exceed this threshold. The Paris Agreement threshold of 1.5 °C refers to long-term warming averaged over 20 years.

Urgent mitigation action is needed, as is climate adaptation.

However, one out of six countries still lack a national adaptation planning instrument, and a significant finance gap remains, with the flow of international public adaptation finance declining since 2020.

Artificial intelligence and Machine Learning: revolutionizing weather forecasting

Thanks to rapid progress, Artificial Intelligence (AI) and Machine Learning (ML) can make skillful weather modelling faster, cheaper and more accessible to lower-income countries with limited computational capacities.

Traditionally, weather forecasting relies on physics-based models through a process known as numerical weather prediction. AI/ML models are trained on reanalysis and observational datasets, making weather forecasting faster and cheaper. Some evaluations have shown the potential of AI/ML for forecasting hazardous events such as tropical cyclones and longer-term predictions of El Niño and La Niña.

There are tremendous opportunities but also many challenges, particularly limited data quality and availability. Current AI/ML models do not include harder-to-predict variables related to the ocean, land, cryosphere and carbon cycle.

Strong global governance is needed to ensure AI/ML serves the global good. Enhanced transparency will be important for building trust and developing standards for responsible use.

Space-based Earth observations

Incredible advancements in recent decades in space-based Earth observations offer vast opportunities for the future.

High-resolution and high-frequency observations of the Earth system are crucial for effective weather forecasting, climate prediction and environmental monitoring.

By leveraging public–private partnerships, innovations in space-based Earth observations can be used to enhance weather, climate, water and related environmental applications.

However, big challenges limit the realization of the full potential of space-based Earth observations in support of global goals. Gaps remain in accurately measuring critical ocean, climate, aerosol and hydrological variables and in covering sparsely observed areas such as the cryosphere.
Additionally, data accessibility and standardization are a problem, particularly for developing countries.

International collaboration, comprehensive governance frameworks for integrated observing systems and innovative financing models are needed to support space-based Earth observation for weather, climate, water and related environmental applications.

Bridging virtual and physical realms: leveraging immersive technologies for water and land management

Socioeconomic impacts and climate change are straining water and land resources, threatening food and water security. Immersive technologies such as digital twins, virtual reality and the metaverse can revolutionize integrated land and water management by offering interactive and data-driven solutions that bridge the physical and digital worlds. From simulating flood and drought events to predicting water flow and accumulation, as well as land degradation, they enhance decision-making and the engagement of diverse actors.

Digital twins are defined as a virtual representation designed to accurately reflect a physical object or system. The metaverse is an integrative ecosystem of virtual worlds that provides immersive experiences.

Challenges include limitations in data availability and quality. There is insufficient access to sustainable funding mechanisms, effective governance frameworks, and lack of public trust and understanding.

International cooperation, knowledge sharing and robust multilateral frameworks are crucial for adopting these innovative solutions.

Towards pathways to sustainable futures: the role of transdisciplinary approaches

Global challenges such as climate change, disaster risk reduction and sustainable development cannot be addressed by one form of knowledge alone – they require a transdisciplinary approach that unites actors across environmental, social and cultural contexts to co-create and implement solutions.

Conventional approaches often focus on understanding the dimensions of natural and social sciences, policy and society separately.

A transdisciplinary approach brings together diverse actors, such as scientists, policymakers, practitioners and civil society, including local and Indigenous communities, to co-create knowledge and develop solutions that are relevant to local contexts. It differs from a multidisciplinary approach, where experts from different disciplines work on the same issue separately.

For instance, engaging scientists, policymakers, practitioners and local and Indigenous communities from the outset enriches understanding of climate change impacts on the ground and offers a more complete perspective.

It also strengthens trust in institutions such as National Meteorological and Hydrological Services (NMHSs).

A future where everyone is protected by life-saving early warning systems

Multi-hazard early warning systems (MHEWS) are critical for protecting lives, livelihoods and the environment. Evidence shows that disaster-related mortality in countries with limited to moderate MHEWS coverage is nearly six times higher than those with substantial to comprehensive coverage.

Progress has been made and more than half of the world’s countries now report having MHEWS. But significant gaps remain.

The Early Warnings for All (EW4All) initiative aims to ensure everyone on Earth is protected from hazardous weather, water, and climate events through life-saving early warning systems by the end of 2027. The initiative underscored the importance of embracing natural and social sciences, technological advances and transdisciplinary approaches.

To scale up action on EW4All across stakeholders, innovation in science, technology and tools such as artificial intelligence (AI), multi-channel and digital communication platforms and citizen science – will be pivotal. By harnessing these advancements and ensuring they are backed by adequate resources, we can make game-changing advancements to ensure that Early Warnings for All becomes a reality for communities all over the world.

SDGs, Targets, and Indicators

1. SDG 13: Climate Action

   • Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters
   • Target 13.2: Integrate climate change measures into national policies, strategies, and planning
   • Target 13.3: Improve education, awareness-raising, and human and institutional capacity on climate change mitigation, adaptation, impact reduction, and early warning
   • Target 13.A: Implement the commitment undertaken by developed-country parties to the United Nations Framework Convention on Climate Change to a goal of mobilizing jointly $100 billion annually by 2020 from all sources to address the needs of developing countries in the context of meaningful mitigation actions and transparency on implementation and fully operationalize the Green Climate Fund through its capitalization as soon as possible
   • Target 13.B: Promote mechanisms for raising capacity for effective climate change-related planning and management in least developed countries and small island developing States, including focusing on women, youth, and local and marginalized communities
   • Indicator 13.1.1: Number of deaths, missing persons, and directly affected persons attributed to disasters per 100,000 population
   • Indicator 13.2.1: Number of countries that have integrated mitigation, adaptation, impact reduction, and early warning into primary, secondary, and tertiary curricula
   • Indicator 13.3.1: Number of countries that have communicated the strengthening of institutional, systemic, and individual capacity-building to implement adaptation, mitigation, and technology transfer, and development actions
   • Indicator 13.A.1: Mobilized amount of United States dollars per year between 2020 and 2025 accountable towards the $100 billion commitment
   • Indicator 13.B.1: Number of least developed countries and small island developing States that are receiving specialized support, and amount of support, including finance, technology, and capacity-building, for mechanisms for raising capacities for effective climate change-related planning and management

2. SDG 9: Industry, Innovation, and Infrastructure

   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors in all countries, in particular developing countries, including, by 2030, encouraging innovation and substantially increasing the number of research and development workers per 1 million people and public and private research and development spending
   • Indicator 9.5.1: Research and development expenditure as a proportion of GDP
   • Indicator 9.5.2: Researchers (in full-time equivalent) per million inhabitants

Table: SDGs, Targets, and Indicators

Source: wmo.int