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With the concept of climate services rapidly climbing research and research-funding agendas worldwide, the time is ripe for a debate about the objectives, scope and content of such services.

Feedbacks between land carbon pools and climate provide one of the largest sources of uncertainty in our predictions of global climate. Estimates of the sensitivity of the terrestrial carbon budget to climate anomalies in the tropics and the identification of the mechanisms responsible for feedback effects remain uncertain. The Amazon basin stores a vast amount of carbon, and has experienced increasingly higher temperatures and more frequent floods and droughts over the past two decades. Here we report seasonal and annual carbon balances across the Amazon basin, based on carbon dioxide and carbon monoxide measurements for the anomalously dry and wet years 2010 and 2011, respectively. We find that the Amazon basin lost 0.48 ± 0.18 petagrams of carbon per year (Pg C yr(-1)) during the dry year but was carbon neutral (0.06 ± 0.1 Pg C yr(-1)) during the wet year. Taking into account carbon losses from fire by using carbon monoxide measurements, we derived the basin net biome exchange (that is, the carbon flux between the non-burned forest and the atmosphere) revealing that during the dry year, vegetation was carbon neutral. During the wet year, vegetation was a net carbon sink of 0.25 ± 0.14 Pg C yr(-1), which is roughly consistent with the mean long-term intact-forest biomass sink of 0.39 ± 0.10 Pg C yr(-1) previously estimated from forest censuses. Observations from Amazonian forest plots suggest the suppression of photosynthesis during drought as the primary cause for the 2010 sink neutralization. Overall, our results suggest that moisture has an important role in determining the Amazonian carbon balance. If the recent trend of increasing precipitation extremes persists, the Amazon may become an increasing carbon source as a result of both emissions from fires and the suppression of net biome exchange by drought.

A consequence of Darwin's 'principle of divergence' is that loss of species can harm the functioning of ecosystems. A study of algal communities in artificial streams suggests that he was right. See Letter p.86 Studies in recent years have suggested that the conservation of biodiversity improves the ability of an ecosystem to retain nutrients and remain productive. These papers have proved controversial, in part because of a lack of direct evidence for a mechanism to explain the phenomenon. Now, in experiments involving manipulation of the number of algal species in model stream systems, Bradley Cardinale provides one such mechanism. Uptake of nitrogen nutrients increased linearly with species richness in response to changes in flow habitats and disturbance regimes. But when niche structure was experimentally removed, the relationship disappeared. This suggests that habitats with more species take greater advantage of the niche opportunities in an environment than do less-species-rich habitats, allowing the more diverse systems to capture a greater fraction of biologically active resources such as nitrogen.

Slides presented at the 102 Annual American Meteorological Society Meeting, as part of the session "Major Weather Events and Impacts of 2021" (paper 6.3 - It's Getting Hot in Here: Real-Time Climate Fingerprints Applied to the 2021 Extreme Heat Season) For more information, please reach out to Daniel Gilford at dgilford@climatecentral.org. Presentation Abstract: Extreme heat was observed and experienced across large portions of the United States in 2021, including during notable record-breaking events in the Pacific Northwest, the Southwest, and along the East coast. The contiguous US experienced its hottest June on record, and excess heat related deaths stretched into the thousands. While more frequent and intense periods of extreme heat are expected consequences of anthropogenic climate change, rapidly and continuously assessing the degree to which human emissions of greenhouse gases increase the likelihood of a specific event remains a challenging technical process. In this study we introduce the Realtime Climate attribution framework and illustrate its application through an analysis of observed 2021 extreme heat events. The framework implements one model-based and two observation-based approaches to produce three distinct attribution assessments, including best estimates and uncertainties. The framework is designed to be flexible across a range of variables and scales, computationally lightweight, and adaptable for impact studies. Using a suite of global climate models, observed global mean temperatures, and local observed daily temperatures, we quantify the extent to which human-driven climate change made 2021 maximum and minimum daily temperature extremes more likely across the United States. Results confirm the continued and growing influence of human-driven climate change in local weather extremes. For instance, we find that the record-breaking high temperatures in June near Phoenix, AZ, were at least 3.25 times more likely because of human activity. Through this framework, we are building the capacity to produce attribution estimates while an event is unfolding. Furthermore, the ability to estimate attribution levels continuously will enhance studies of extreme heat impacts on human health, along with other socioeconomic or influences.

In the digital society, individuals play different roles depending on the situation they are placed in: they are consumers when they purchase a good, citizens when they vote for elections, content providers when they post information on a platform, and data subjects when their data is collected. Public authorities have thus far regulated citizens and the data collected on their different roles in silos (e.g., bankruptcy registrations, social welfare databases), resulting in inconsistent decisions, redundant paperwork, and delays in processing citizen requests. Data silos are considered to be inefficient both for companies and governments. Big data and data analytics are disrupting these silos allowing the different roles of individuals and the respective data to converge. In practice, this happens in several countries with data sharing arrangements or ad hoc data requests. However, breaking down the existing structure of information silos in the public sector remains problematic. While big data disrupts artificial silos that may not make sense in the digital society and promotes a truly efficient digitalization of data, removing information out of its original context may alter its meaning and violate the privacy of citizens. In addition, silos ensure that citizens are not assessed in one field by information generated in a totally different context. This chapter discusses how big data and data analytics are changing information silos and how digital technology is challenging citizens’ autonomy and right to privacy and data protection. This chapter also explores the need for a more integrated approach to the study of information, particularly in the public sector.

Recently, in the economics literature, several papers have put forward arguments for using a declining discount rate in social-cost benefit analysis. This paper examines the impact of employing a declining discount rate on the social cost of carbon-the marginal social damage from a ton of emitted carbon. Six declining discounting schemes are implemented in the FUND 2.8 integrated assessment model, including the recent amendments to the Green Book of HM Treasury (Treasury, H.M., 2003. The Greenbook: Appraisal and Evaluation in Central Government. TSO, London). We find that using a declining schedule of discount rates increases the social cost of carbon estimate by as little as 10% or by as much as a factor of 40, depending upon the scenario selected. Although the range of plausible estimates is large, using declining discounting schemes in FUND 2.8 in most cases does not yield values at the £70/tC level suggested by UK DEFRA [Clarkson, R., Deyes, K., 2002. Estimating the social cost of carbon emissions. Government Economic Service Working Paper. HM Treasury, London]. Indeed, only at the higher end of the values of social cost of carbon found here would many climate change related policies - such as the Kyoto Protocol - pass a cost-benefit analysis. This conclusion, however, does not necessarily undermine the ethical and political economic reasons for supporting international collective action on climate change. © 2006 Elsevier Ltd. All rights reserved.

Global climate models suggest enhanced warming of the tropical mid and upper troposphere, with larger temperature rise rates at higher elevations. Changes in fire activity are amongst the most significant ecological consequences of rising temperatures and changing hydrological properties in mountainous ecosystems, and there is a global evidence of increased fire activity with elevation. Whilst fire research has become popular in the tropical lowlands, much less is known of the tropical high Andean region (>2000 masl, from Colombia to Bolivia). This study examines fire trends in the high Andes for three ecosystems, the Puna, the Paramo and the Yungas, for the period 1982-2006. We pose three questions: (i) is there an increased fire response with elevation? (ii) does the El Niño- Southern Oscillation control fire activity in this region? (iii) are the observed fire trends human driven (e.g., human practices and their effects on fuel build-up) or climate driven? We did not find evidence of increased fire activity with elevation but, instead, a quasicyclic and synchronous fire response in Ecuador, Peru and Bolivia, suggesting the influence of high-frequency climate forcing on fire responses on a subcontinental scale, in the high Andes. ENSO variability did not show a significant relation to fire activity for these three countries, partly because ENSO variability did not significantly relate to precipitation extremes, although it strongly did to temperature extremes. Whilst ENSO did not individually lead the observed regional fire trends, our results suggest a climate influence on fire activity, mainly through a sawtooth pattern of precipitation (increased rainfall before fire-peak seasons (t-1) followed by drought spells and unusual low temperatures (t0), which is particularly common where fire is carried by low fuel loads (e.g., grasslands and fine fuel). This climatic sawtooth appeared as the main driver of fire trends, above local human influences and fuel build-up cyclicity.

Representing model uncertainty is important for both numerical weather and climate prediction. Sto- chastic parametrisation schemes are commonly used for this purpose in weather prediction, while perturbed param- eter approaches are widely used in the climate community. The performance of these two representations of model uncertainty is considered in the context of the idealised Lorenz '96 system, in terms of their ability to capture the observed regime behaviour of the system. These results are applicable to the atmosphere, where evidence points to the existence of persistent weather regimes, and where it is desirable that climate models capture this regime behav- iour. The stochastic parametrisation schemes considerably improve the representation of regimes when compared to a deterministic model: both the structure and persistence of the regimes are found to improve. The stochastic para- metrisation scheme represents the small scale variability present in the full system, which enables the system to explore a larger portion of the system's attractor, improv- ing the simulated regime behaviour. It is important that temporally correlated noise is used in the stochastic para- metrisation—white noise schemes performed similarly to the deterministic model. In contrast, the perturbed param- eter ensemble was unable to capture the regime structure of the attractor, with many individual members exploring only one regime. This poor performance was not evident in other climate diagnostics. Finally, a 'climate change' experiment