Climate Resilience and Sustainability最新文献

Earth System Models (ESMs) project climate change, but they often contain biases in their estimates of contemporary climate that propagate into simulated futures. Land models translate climate projections into surface impacts, but these will be inaccurate if ESMs have substantial errors. Bias concerns are relevant for terrestrial physiological processes which often respond non-linearly (i.e. contain threshold responses) and are therefore sensitive to absolute environmental conditions as well as changes. We bias-correct the UK Met Office ESM, HadGEM2-ES, against the CRU–JRA observation-based gridded estimates of recent climate. We apply the derived bias corrections to future projections by HadGEM2-ES for the RCP8.5 scenario of future greenhouse gas concentrations. Focusing on South America, the bias correction includes adjusting for ESM estimates that, annually, are approximately 1 degree too cold, for comparison against 21st Century warming of around 4 degrees. Locally, these values can be much higher. The ESM is also too wet on average, by approximately 1 mm·day⁻¹, which is substantially larger than the mean predicted change. The corrected climate fields force the Joint UK Land Environment Simulator (JULES) dynamic global vegetation model to estimate land surface changes, with an emphasis on the carbon cycle. Results show land carbon sink reductions across South America, and in some locations, the net land–atmosphere CO₂ flux becomes a source to the atmosphere by the end of this century. Transitions to a CO₂ source is where increases in plant net primary productivity are offset by larger enhancements in soil respiration. Bias-corrected simulations estimate the rise in South American land carbon stocks between pre-industrial times and the end of the 2080s is ∼12 GtC lower than that without climate bias removal, demonstrating the importance of merging historical observational meteorological forcing with ESM diagnostics. We present evidence for a substantial climate-induced role of greater soil decomposition in the fate of the Amazon carbon sink.

More frequent and stronger flood hazards in the last two decades have caused considerable environmental and socio-economic losses in many regions of the Amazon basin. It is therefore critical to advance predictions for flood levels, with adequate lead times, to provide more effective and earlier warnings to safeguard lives and livelihoods. Water-level variations in large, low-lying, free-flowing river systems in the Amazon basin, such as the Negro River, follow large-scale precipitation anomalies. This offers an opportunity to predict maximum water levels using observed antecedent rainfall. This study aims to investigate possible improvements in the performance and extension of the lead time of existing operational statistical forecasts for annual maximum water level of the Negro River at Manaus, occurring between May and July. We develop forecast models using multiple linear regression methods, to produce forecasts that can be issued in March, February and January. Potential predictors include antecedent catchment rainfall and water levels, large-scale modes of climate variability and the long-term linear trend in water levels. Our statistical models gain one month of lead time against existing models for same skill level, but are only moderately better than existing models at similar lead times. All models lose performance at longer lead times, as expected. However, our forecast models can issue skilful operational forecasts in March or earlier. We show the forecasts for the Negro River maximum water level at Manaus for 2020 and 2021.

Timely spatially explicit warning of areas with high fire occurrence probability is an important component of strategic plans to prevent and monitor fires within South American (SA) Protected Areas (PAs). In this study, we present a five-level alert system, which combines both climatological and anthropogenic factors, the two main drivers of fires in SA. The alert levels are: High Alert, Alert, Attention, Observation and Low Probability. The trend in the number of active fires over the past three years and the accumulated number of active fires over the same period were used as indicators of intensification of human use of fire in that region, possibly associated with ongoing land use/land cover change (LULCC). An ensemble of temperature and precipitation gridded output from the GloSea5 Seasonal Forecast System was used to indicate an enhanced probability of hot and dry weather conditions that combined with LULCC favour fire occurrences. Alerts from this system were first issued in August 2020, for the period ranging from August to October (ASO) 2020. Overall, 50% of all fires observed during the ASO 2017–2019 period and 40% of the ASO 2020 fires occurred in only 29 PAs were all categorized in the top two alert levels. In categories mapped as High Alert level, 34% of the PAs experienced an increase in fires compared with the 2017–2019 reference period, and 81% of the High Alert false alarm registered fire occurrence above the median. Initial feedback from stakeholders indicates that these alerts were used to inform resource management in some PAs. We expect that these forecasts can provide continuous information aiming at changing societal perceptions of fire use and consequently subsidize strategic planning and mitigatory actions, focusing on timely responses to a disaster risk management strategy. Further research must focus on the model improvement and knowledge translation to stakeholders.

The semiarid lands of Northeast Brazil represent one of the most densely populated regions of the country. Rainfall variability together with land degradation and large-scale poverty in rural areas makes this region vulnerable to droughts. Most of the agriculture in this region is rainfed and deficient rainfall leads to severe drought impacts. In this review, we examine different short- and long-term strategies directed to cope with possible impacts of droughts proposed by the government, farmers, civil society, and the private sector. These are approaches to adaptation to drought in the Northeast of Brazil, and among them, we have agricultural management and soil conservation and better management of water resources. Other actions include seasonal climate forecasts and funds transfer and credits to affected small-scale farmers. Although some of these actions are for the short term and may help to survive the drought situation, they may be only postdisaster mitigation options that do not improve adaptive capacity. They favor maladaptation and create dependency of farmers to government actions. Some experiences such as AdaptaSertão show potential benefits for small-scale farmers. We identify key challenges for moving toward a more holistic risk management approach and highlight the need to integrate actions and tools for adaptation, combining technology-based solutions with in-depth knowledge of local and regional social, economic, and cultural aspects, among them seasonal climate forecasts and drought impacts studies, among some other proactive predisaster ways, rather than reactive postdisaster actions. Adaptation strategies must increase long-term resilience of food production in the Brazilian Northeast, going beyond an individual drought event.

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.