Climate Resilience and Sustainability最新文献

The climate modeling techniques of event attribution enable systematic assessments of the extent that anthropogenic climate change may be altering the probability or magnitude of extreme events. In the consecutive years of 2018, 2019, and 2020, rainfalls caused repeated flooding impacts in the lower Parnaíba River in Northeastern Brazil. We studied the effect that alterations in precipitation resulting from human influences on the climate had on the likelihood of flooding using two ensembles of the HadGEM3-GA6 atmospheric model: one driven by both natural and anthropogenic forcings; and the other driven only by natural atmospheric forcings, with anthropogenic changes removed from sea surface temperatures and sea ice patterns. We performed hydrological modeling to base our assessments on the peak annual streamflow. The change in the likelihood of flooding was expressed in terms of the ratio between probabilities of threshold exceedance estimated for each model ensemble. With uncertainty estimates at the 90% confidence level, the median (5% 95%) probability ratio at the threshold for flooding impacts in the historical period (1982–2013) was 1.12 (0.97 1.26), pointing to a marginal contribution of anthropogenic emissions by about 12%. For the 2018, 2019, and 2020 events, the median (5% 95%) probability ratios at the threshold for flooding impacts were higher at 1.25 (1.07 1.46), 1.27 (1.12 1.445), and 1.37 (1.19 1.59), respectively; indicating that precipitation change driven by anthropogenic emissions has contributed to the increase of likelihood of these events by about 30%. However, there are other intricate hydrometeorological and anthropogenic processes undergoing long-term changes that affect the flood hazard in the lower Parnaíba River. Trend and flood frequency analyses performed on observations showed a nonsignificant long-term reduction of annual peak flow, likely due to decreasing precipitation from natural climate variability and increasing evapotranspiration and flow regulation.

In January 2020, an extreme precipitation event occurred over southeast Brazil, with the epicentre in Minas Gerais state. Although extreme rainfall frequently occurs in this region during the wet season, this event led to the death of 56 people, drove thousands of residents into homelessness, and incurred millions of Brazilian Reais (BRL) in financial loss through the cascading effects of flooding and landslides. The main question that arises is: To what extent can we blame climate change? With this question in mind, our aim was to assess the socioeconomic impacts of this event and whether and how much of it can be attributed to human-induced climate change. Our findings suggest that human-induced climate change made this event >70% more likely to occur. We estimate that >90,000 people became temporarily homeless, and at least BRL 1.3 billion (USD 240 million) was lost in public and private sectors, of which 41% can be attributed to human-induced climate change. This assessment brings new insights about the necessity and urgency of taking action on climate change, because it is already effectively impacting our society in the southeast Brazil region. Despite its dreadful impacts on society, an event with this magnitude was assessed to be quite common (return period of $�\sim$4 years). This calls for immediate improvements on strategic planning focused on mitigation and adaptation. Public management and policies must evolve from the disaster response modus operandi in order to prevent future disasters.

Forecasts of tropical ecosystem C cycling diverge among models due to differences in simulation of internal processes such as turnover, or transit times, of carbon pools. Estimates of these processes for the recent past are needed to test model representations, and so build confidence in model forecasts within and across biomes. Here, we evaluate carbon cycle process representation in two land surface models [Joint UK Land Environment Simulator (JULES) and Integrated Model of Land Surface Processes (INLAND)] for the period 2001–10 across Brazilian biomes. Model outputs are evaluated using the ILAMB system. Probabilistic benchmarking data were created using the carbon data model framework that assimilates observational times series of leaf area index and maps of woody biomass and soil C. New custom uncertainty metrics assess if models are within benchmark uncertainties. Simulations are better in homogeneous areas of vegetation type, and are less robust at ecotones between biomes, likely due to disturbance effects and parameter errors. Gross biosphere-atmosphere fluxes are robustly modelled across Brazil. However, benchmark uncertainty is too high on net ecosystem exchange to provide an accurate evaluation of the models. The LSMs have significant differences in internal carbon allocation and the dynamics of the different C pools. JULES models dead C stocks more accurately while living C stocks are best resolved for INLAND. JULES' over-estimate of the C wood pool results from over-estimation of both inputs to wood and the transit time of wood. INLAND's under-estimate of dead C stocks arises from an under-estimate of the transit time of dead organic matter. The models are better at simulating annual averages than seasonal variation of fluxes. Analyses of monthly net C exchanges show that INLAND correctly simulates seasonality, but over-estimates amplitudes, whereas JULES correctly simulates the annual amplitudes, but is out of phase with the benchmark.

This study aims to investigate local-scale meteorological conditions associated with large fires in Brazil during recent decades. We assess whether there are large fire types with preceding predictors. Our results show that large fires, defined with a threshold of a daily burned area >95th percentile of the historical record, mainly occur in August and September in Brazil, and Amazônia and Cerrado experience much higher numbers of large fires than the other biomes. There are two large fire types that have robust meteorological signatures: (1) a wind driven type, characterized by peak wind speed on the day of the fire, and anomalously high wind speed a few (∼3) days before and after the fire; and (2) a Hot-Drought driven type, characterized by anomalously high temperature, low relative humidity, and consistent drought conditions indicated by anomalously high fuel aridity starting as far back as 5 months prior to the fires. A third one is characterized by no anomalous meteorological conditions. The wind driven type most frequently occurs in southern and southeastern Amazônia, Pantanal, and western and northern-to-central Cerrado, with some occurrences over the western Caatinga region bordering Cerrado, southern Cerrado, and southern Mata Atlântica; whereas the Hot-Drought driven type most frequently occurs in southern and southeastern Amazônia, Pantanal and western and northern-to-central Cerrado, with some occurrences over the western Caatinga region bordering Cerrado, southern Cerrado, central-to-southern Mata Atlântica, and a few occurrences over Northern Brazil where the Amazônia meets Roraima. Southern and southeastern Amazônia, Pantanal and western and northern-to-central Cerrado are the major large fire prone regions. Our results highlight that understanding the temporal and spatial variability of the meteorological conditions associated with large fires is essential for developing spatially explicit forecasting, and future projections of large fire hazards under climate change in Brazil, in particular the Hot-Drought driven type.

Soil moisture information is essential to monitoring of the intensity of droughts, the start of the rainy season, planting dates and early warnings of yield losses. We assess spatial and temporal trends of drought over the Brazilian semiarid region by combining soil moisture observations from 360 stations, root zone soil moisture from a leading land surface model, and a vegetation health index from remote sensing. The soil moisture dataset was obtained from the network of stations maintained by the National Center of Monitoring and Early Warning of Natural Disasters (Cemaden), in Brazil. Soil water content at 10 to 35 cm depth, for the period 1979–2018, was obtained from running the JULES land surface model (the Joint UK Land Environment Simulator). The modelled soil moisture was correlated with measurements in the common period of 2015–2018, resulting in an average correlation coefficient of 0.48 across the domain. The standardized soil moisture anomaly (SMA) was calculated for the long-term modelled soil moisture and revealed strong negative values during well-known drought periods in the region, especially during El-Niño years. The performance of SMA in identifying droughts during the first 2 months of the raining and cropping season was similar to the Standardized Precipitation Index (SPI), commonly used for drought assessment: 12–14 events were identified by both indices. Finally, the temporal relationship between both SMA and SPI with the Vegetation Health Index (VHI) was assessed using the cross-wavelet transform. The results indicated lagged correlations of 1 to 1.5 months in the annual scale, suggesting that negative trends in SMA and SPI can be an early warning to yield losses during the growing season. Public policies on drought assessment should consider the combination of multiple drought indices, including soil moisture anomaly.

Unprecedented fire events in recent years are leading to a demand for improved understanding of how climate change is already affecting fires, and how this could change in the future. Increased fire activity in South America is one of the most concerning of all the recent events, given the potential impacts on local ecosystems and the global climate from the loss of large carbon stores under future socio-environmental change. However, due to the complexity of interactions and feedbacks, and lack of complete representation of fire biogeochemistry in many climate models, there is currently low agreement on whether climate change will cause fires to become more or less frequent in the future, and what impact this will have on ecosystems. Here we use the latest climate simulations from the UK Earth System Model UKESM1 to understand feedbacks in fire, dynamic vegetation, and terrestrial carbon stores using the JULES land surface model, taking into account future scenarios of change in emissions and land use. Based on evaluation of model performance for the present day, we address the specific policy-relevant question: how much fire-induced carbon loss will there be over South America at different global warming levels in the future? We find that burned area and fire emissions are projected to increase in the future due to hotter and drier conditions, which leads to large reductions in carbon storage, especially when combined with increasing land-use conversion. The model simulates a 30% loss of carbon at 4°C under the highest emission scenario, which could be reduced to 7% if temperature rise is limited to 1.5°C. Our results provide a critical assessment of ecosystem resilience under future climate change, and could inform the way fire and land-use is managed in the future to reduce the most deleterious impacts of climate change.

We are pleased to present Climate Resilience and Sustainability (CRS), an exciting new journal that is right for this decade and this century. The journal is online only and open access, reflecting our goal to make the papers available to everyone. We are aiming for fast publication, while ensuring a robust peer-review process. The journal is published as a joint effort between the Royal Meteorological Society and Wiley.

With accelerating climate change and impacts, effective action requires bringing together sound knowledge from multiple disciplines as well as “on the ground” practice. CRS aims to publish such interdisciplinary research and practice to scale-up solutions and actions. We would also like to receive papers that reflect action research and co-production of knowledge.

Our motivation to establish this journal reflects the change in focus over the past few decades from physical aspects of climate science to solutions for climate resilient and sustainable development. In the early 1980s, the focus of climate change research was on detecting the changes in the atmosphere and ocean, and on using models to understand these changes. Sophisticated models were developed for the atmosphere–ocean systems to understand and to project the likely changes on specific variables such as atmospheric greenhouse gas concentration, air/ocean temperatures, and precipitation. By the early 1990s, the changes in the atmosphere–ocean systems were shown to be affecting societies and ecosystems. To reflect this, by the late 1990s, the Intergovernmental Panel on Climate Change (IPCC) made the change to the title of Working Group II Report from “Impacts, Adaptations and Mitigation of Climate Change: Scientific-Technical Analyses” to “Impacts, Adaptation and Vulnerability.” This heralded an increased focus on vulnerability, resilience and sustainability of people, ecosystems, and economies. IPCC Working Group II and the IPCC Synthesis Reports also highlighted the need for climate resilient and sustainable development pathways. In other discussion within the United Nations, the Millennium Development Goals evolved into the 2030 Agenda for Sustainable Development. Collectively this strengthened the emphasis on sustainability and climate resilience as part of the ambitions for development of all countries.

These three categories also aim to bring together researchers and practitioners across a range of relevant disciplines into a single readership. We are certain that such a process will help find timely resilient solutions for our world that is being affected by escalating impacts of climate change and development activities.

CRS is also planning to publish special issues that focus on challenges and resilient solutions in specific countries or regions and on specific sectors. We already have a range of special issues being planned and in production, for example, country level research and assessment toward climate resilient

Climate change will increase the frequency of heatwaves in the United Kingdom and reduce the frequency of cold spells. This paper evaluates the effect of changes in climate as represented by UKCP18 climate projections on a series of indicators of heat and cold extremes relevant to policy in the United Kingdom. These indicators are expressed in terms of current critical thresholds beyond which alerts are issued or specific actions implemented, rather than impacts on health and well-being. The frequency and duration of heatwave and heat–health alerts increase under all scenarios, with the greatest absolute number of events in the south and east of England where the chance of hot weather events affecting worker productivity doubles by the 2020s. Cold weather events – triggering health and social care plans and benefit payments – will become less frequent, but the effects of climate change on cold events are much smaller than on hot events and they will continue to occur. Until at least the 2040s, the projected effects of climate change do not depend strongly on the assumed change in global emissions, and the range in possible changes is primarily determined by uncertainty in the change in temperature in the United Kingdom for a given emissions pathway. Beyond the 2050s, the impacts are strongly dependent on future emissions. Impacts in a high-emissions world will be considerably larger than in low-emissions world. The projected increase in heatwave alerts, and the duration and intensity of heatwaves, implies not only a need to review heatwave emergency planning arrangements – looking in particular at what should become regarded as ‘normal’ summer weather – but also increased efforts to reduce vulnerability to extreme heat events. At the same time, cold weather events will still continue to occur with a sufficient frequency that plans need to be maintained.