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As the Arctic warms, vegetation is responding, and satellite measures indicate widespread greening at high latitudes. This ‘greening of the Arctic’ is among the world’s most important large-scale ecological responses to global climate change. However, a consensus is emerging that the underlying causes and future dynamics of so-called Arctic greening and browning trends are more complex, variable and inherently scale-dependent than previously thought. Here we summarize the complexities of observing and interpreting high-latitude greening to identify priorities for future research. Incorporating satellite and proximal remote sensing with in-situ data, while accounting for uncertainties and scale issues, will advance the study of past, present and future Arctic vegetation change.As tundra ecosystems respond to rapid Arctic warming, satellite records suggest a widespread greening. This Perspective highlights the challenges of interpreting complex Arctic greening trends and provides direction for future research by combining ecological and remote sensing approaches.

Twenty-five years since foundational publications on valuing ecosystem services for human well-being 1 , 2 , addressing the global biodiversity crisis 3 still implies confronting barriers to incorporating nature’s diverse values into decision-making. These barriers include powerful interests supported by current norms and legal rules such as property rights, which determine whose values and which values of nature are acted on. A better understanding of how and why nature is (under)valued is more urgent than ever 4 . Notwithstanding agreements to incorporate nature’s values into actions, including the Kunming-Montreal Global Biodiversity Framework (GBF) 5 and the UN Sustainable Development Goals 6 , predominant environmental and development policies still prioritize a subset of values, particularly those linked to markets, and ignore other ways people relate to and benefit from nature 7 . Arguably, a ‘values crisis’ underpins the intertwined crises of biodiversity loss and climate change 8 , pandemic emergence 9 and socio-environmental injustices 10 . On the basis of more than 50,000 scientific publications, policy documents and Indigenous and local knowledge sources, the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) assessed knowledge on nature’s diverse values and valuation methods to gain insights into their role in policymaking and fuller integration into decisions 7 , 11 . Applying this evidence, combinations of values-centred approaches are proposed to improve valuation and address barriers to uptake, ultimately leveraging transformative changes towards more just (that is, fair treatment of people and nature, including inter- and intragenerational equity) and sustainable futures. Following a wide-ranging review of studies, reports and policies about nature’s multiple values, combinations of values-centred approaches are proposed to improve valuation of nature, address barriers to uptake in decision-making, and make transformative changes towards more just and sustainable futures.

As climate change research becomes increasingly applied, the need for actionable information is growing rapidly. A key aspect of this requirement is the representation of uncertainties. The conventional approach to representing uncertainty in physical aspects of climate change is probabilistic, based on ensembles of climate model simulations. In the face of deep uncertainties, the known limitations of this approach are becoming increasingly apparent. An alternative is thus emerging which may be called a ‘storyline’ approach. We define a storyline as a physically self-consistent unfolding of past events, or of plausible future events or pathways. No a priori probability of the storyline is assessed; emphasis is placed instead on understanding the driving factors involved, and the plausibility of those factors. We introduce a typology of four reasons for using storylines to represent uncertainty in physical aspects of climate change: (i) improving risk awareness by framing risk in an event-oriented rather than a probabilistic manner, which corresponds more directly to how people perceive and respond to risk; (ii) strengthening decision-making by allowing one to work backward from a particular vulnerability or decision point, combining climate change information with other relevant factors to address compound risk and develop appropriate stress tests; (iii) providing a physical basis for partitioning uncertainty, thereby allowing the use of more credible regional models in a conditioned manner and (iv) exploring the boundaries of plausibility, thereby guarding against false precision and surprise. Storylines also offer a powerful way of linking physical with human aspects of climate change.

Wind power, a rapidly growing alternative energy source, has been threatened by reductions in global average surface wind speed, which have been occurring over land since the 1980s, a phenomenon known as global terrestrial stilling. Here, we use wind data from in situ stations worldwide to show that the stilling reversed around 2010 and that global wind speeds over land have recovered. We illustrate that decadal-scale variations of near-surface wind are probably determined by internal decadal ocean–atmosphere oscillations, rather than by vegetation growth and/or urbanization as hypothesized previously. The strengthening has increased potential wind energy by 17 ± 2% for 2010 to 2017, boosting the US wind power capacity factor by ~2.5% and explains half the increase in the US wind capacity factor since 2010. In the longer term, the use of ocean–atmosphere oscillations to anticipate future wind speeds could allow optimization of turbines for expected speeds during their productive life spans.

The Arctic has warmed more than twice as fast as the global average since the late twentieth century, a phenomenon known as Arctic amplification (AA). Recently, there have been considerable advances in understanding the physical contributions to AA, and progress has been made in understanding the mechanisms that link it to midlatitude weather variability. Observational studies overwhelmingly support that AA is contributing to winter continental cooling. Although some model experiments support the observational evidence, most modelling results show little connection between AA and severe midlatitude weather or suggest the export of excess heating from the Arctic to lower latitudes. Divergent conclusions between model and observational studies, and even intramodel studies, continue to obfuscate a clear understanding of how AA is influencing midlatitude weather.

Nitrogen (N) and phosphorus (P) limitation constrains the magnitude of terrestrial carbon uptake in response to elevated carbon dioxide and climate change. However, global maps of nutrient limitation are still lacking. Here we examined global N and P limitation using the ratio of site-averaged leaf N and P resorption efficiencies of the dominant species across 171 sites. We evaluated our predictions using a global database of N- and P-limitation experiments based on nutrient additions at 106 and 53 sites, respectively. Globally, we found a shift from relative P to N limitation for both higher latitudes and precipitation seasonality and lower mean annual temperature, temperature seasonality, mean annual precipitation and soil clay fraction. Excluding cropland, urban and glacial areas, we estimate that 18% of the natural terrestrial land area is significantly limited by N, whereas 43% is relatively P limited. The remaining 39% of the natural terrestrial land area could be co-limited by N and P or weakly limited by either nutrient alone. This work provides both a new framework for testing nutrient limitation and a benchmark of N and P limitation for models to constrain predictions of the terrestrial carbon sink.Spatial patterns in the phosphorus and nitrogen limitation in natural terrestrial ecosystems are reported from analysis of a global database of the resorption efficiency of nutrients by leaves.

Land ecosystems contribute to climate change mitigation by taking up approximately 30% of anthropogenically emitted carbon. However, estimates of the amount and distribution of carbon uptake across the world's ecosystems or biomes display great uncertainty. The latter hinders a full understanding of the mechanisms and drivers of land carbon uptake, and predictions of the future fate of the land carbon sink. The latter is needed as evidence to inform climate mitigation strategies such as afforestation schemes. To advance land carbon cycle modeling, we have developed a matrix approach. Land carbon cycle models use carbon balance equations to represent carbon exchanges among pools. Our approach organizes this set of equations into a single matrix equation without altering any processes of the original model. The matrix equation enables the development of a theoretical framework for understanding the general, transient behavior of the land carbon cycle. While carbon input and residence time are used to quantify carbon storage capacity at steady state, a third quantity, carbon storage potential, integrates fluxes with time to define dynamic disequilibrium of the carbon cycle under global change. The matrix approach can help address critical contemporary issues in modeling, including pinpointing sources of model uncertainty and accelerating spin‐up of land carbon cycle models by tens of times. The accelerated spin‐up liberates models from the computational burden that hinders comprehensive parameter sensitivity analysis and assimilation of observational data to improve model accuracy. Such computational efficiency offered by the matrix approach enables substantial improvement of model predictions using ever‐increasing data availability. Overall, the matrix approach offers a step change forward for understanding and modeling the land carbon cycle. Plain Language Summary Earth system models (ESMs) are the tools we have to predict future states of climate and ecosystems. However, land carbon cycle models, a critical component of ESMs, are highly diverse in both structures and predictions, hindering our ability to obtain consistent future projections. The latter is needed as part of the evidence base to inform climate change mitigation strategies. This paper describes a matrix approach that unifies land carbon cycle models in one matrix form. The matrix models offer consistency and simplicity in structure that make the models analytically tractable. In addition, the matrix approach provides a theoretical framework for understanding the general behavior of the land carbon cycle. More importantly, the matrix approach solves some key contemporary issues in land carbon cycle modeling, such as pinning down sources of model uncertainty and accelerating spin‐up. The accelerated spin‐up speeds up land carbon cycle simulations by tens of times, making it feasible to perform parameter sensitivity analysis and data assimilation to constrain models with big data. Overall, the matrix approach represents a step change forward for understanding and modeling the land carbon cycle. Key Points The matrix approach unifies land carbon cycle models in a matrix form and thus helps gain simplicity in model structure The matrix approach provides a theoretical framework to understand the general behavior of land carbon cycle It helps address contemporary issues in land carbon cycle modeling, including pinning down model uncertainty and accelerating spin‐up

Algal blooms constitute an emerging threat to global inland water quality, yet their spatial and temporal distribution at the global scale remains largely unknown. Here we establish a global bloom database, using 2.91 million Landsat satellite images from 1982 to 2019 to characterize algal blooms in 248,243 freshwater lakes, representing 57.1% of the global lake area. We show that 21,878 lakes (8.8%) spread across six continents have experienced algal blooms. The median bloom occurrence of affected lakes was 4.6%, but this frequency is increasing; we found increased bloom risks in the 2010s, globally (except for Oceania). The most pronounced increases were found in Asia and Africa, mostly in developing countries that remain reliant on agricultural fertilizer. As algal blooms continue to expand in scale and magnitude, this baseline census will be vital towards future risk assessments and mitigation efforts. Algal blooms are occurring more frequently, as seen in a global database compiled from satellite imagery from the past few decades.

The ongoing emission of greenhouse gases (GHGs) is triggering changes in many climate hazards that can impact humanity. We found traceable evidence for 467 pathways by which human health, water, food, economy, infrastructure and security have been recently impacted by climate hazards such as warming, heatwaves, precipitation, drought, floods, fires, storms, sea-level rise and changes in natural land cover and ocean chemistry. By 2100, the world’s population will be exposed concurrently to the equivalent of the largest magnitude in one of these hazards if emmisions are aggressively reduced, or three if they are not, with some tropical coastal areas facing up to six simultaneous hazards. These findings highlight the fact that GHG emissions pose a broad threat to humanity by intensifying multiple hazards to which humanity is vulnerable.