Climate Resilience and Sustainability最新文献

The Climate Science for Service Partnership Brazil (CSSP-Brazil) project provides Brazil and UK partners the opportunity to address important challenges faced by the climate modeling community, including the need to develop subseasonal and seasonal prediction and climate projection services. This paper provides an overview of the climate modeling and prediction research conducted through CSSP-Brazil within the context of a framework to advance climate prediction services in Brazil that includes a research-to-services (R2S) and a services-to-research (S2R) feedback pathway. The paper also highlights plans to advance scientific understanding and capability to produce beneficial climate knowledge and new products to improve climate prediction services to support decisions in various industries in Brazil. Policy-relevant outcomes from climate modeling and prediction exercises illustrated in this paper include supporting stakeholders with climate information provided from weeks to months ahead for (a) improving water management strategies for human consumption, navigation, and agricultural and electricity production; (b) defining crop variety and calendars for food production; and (c) diversifying energy production with alternatives to hydropower.

Land–atmosphere feedbacks, through water and energy exchanges, provide subseasonal-to-seasonal predictability of the hydrological cycle. We analyse subseasonal land–atmosphere coupling over South America (SA) during extended austral summer for the soil moisture-to-precipitation and soil moisture-to-air temperature feedback pathways. We evaluate subseasonal hindcasts from global forecasting systems from the UK Met Office, the National Centers for Environmental Prediction (NCEP), the European Centre for Medium Range Weather Forecasts and the Center for Weather Forecast and Climate Studies (CPTEC), for the common period of 1999–2010, against two reanalyses. Biases in land–atmosphere states are established in the first week of hindcasts and increase with lead time. By Week 5, all the models only demonstrate good performance over northern, northeastern and southeastern SA for soil moisture and evapotranspiration and over tropical and subtropical SA for temperature. The hindcasts show stronger coupling at longer lead–lag between variables than reanalyses. Our results highlight possible deficiencies in feedbacks between soil moisture and precipitation in CPTEC and NCEP forecasts over the Amazon due to initial dry soil moisture biases, and in feedbacks between soil moisture and temperature for all four investigated models over southeastern SA due to erroneous representations of evapotranspiration.

The seasonal response of rainfall over tropical South America to a shutdown in the Atlantic Meridional Overturning Circulation (AMOC) is examined, in HadGEM3 model simulations where freshwater is added to the north Atlantic. Potential biases in these simulations are explored by comparing the unperturbed simulation with observations. In this simulation, in years when the latitude of the model Atlantic Intertropical Convergence Zone (ITCZ) is realistic, the model provides a reasonable simulation of the spatial and seasonal variation in regional-scale rainfall over tropical South America. However, some climatological mean rainfall biases over this region are attributed to the climatological southward bias in the Atlantic ITCZ. Under an AMOC shutdown, the rainfall changes over tropical South America are largely associated with a southward shift of the Atlantic ITCZ. The large seasonal variation in rainfall change over tropical South America is linked primarily with the variation in the location of peak rainfall (itself driven largely by variation in the latitude of peak solar insolation and by the lagged variation in Atlantic ITCZ). The simulated rainfall changes appear to be biased in some months by the southward bias in the Atlantic ITCZ, including a possible overestimation of drying in March and June. In addition, the Atlantic ITCZ in HadGEM3 tends to shift too far in both the seasonal cycle (as reported in other models) and in inter-annual variability. Excessive inter-annual variability may arise because the model ITCZ is too close to the equator, combined with an increase in variability near the equator. Further understanding of what drives the variability in ITCZ latitude, and how that relates to ITCZ shifts under an AMOC shutdown, is suggested as a future research priority.

This paper assesses how well the CPTEC/INPE Brazilian Global Atmospheric Model (BAM-1.2) and the atmospheric component of the UK Met Office Hadley Centre Global Environment Model (HadGEM3-GC3.1) represent the main South American monsoon features. Climatological (1981–2010) ensemble means of Atmospheric Model Intercomparison Project (AMIP)-type climate simulations are evaluated. The assessment evaluated the models’ ability to represent the South America austral summer and winter precipitation contrast and associated circulation, key South American monsoon system elements, the association between south-east Brazil and South America precipitation, and climatological (1997/1998 to 2013/2014) distributions of rainy season onset and demise dates over south-east Brazil (15°S–25°S, 40°W–50°W) and the core monsoon region (10°S–20°S, 45°W–55°W). Despite some identified deficiencies, both models depict the monsoon region and represent the main features, including (1) the north-west–south-east precipitation band and associated ascending motion over central South America; (2) the upper-level Bolivian High and the north-east South America trough during the summer; (3) the lower-level South Atlantic and Pacific subtropical anti-cyclones and (4) the low-level jet east of the Andes. Both models represent upper-level divergence and lower-level convergence over the core monsoon region, and upper-level convergence and lower-level divergence over the Pacific and Atlantic anti-cyclones associated with the regional Walker circulation during the pre-monsoon (spring) and peak monsoon (summer) seasons. Convection over South America is weaker in BAM-1.2 than observed, consistent with continental precipitation deficit. The models reproduce the dipole-like precipitation pattern between south-east Brazil and south-eastern South America during the austral summer but overestimate these patterns spatial extent over the South Atlantic. Both models simulate the main observed climatological features of rainy season onset and demise dates for the two above defined investigated regions. HadGEM3 overestimates onset dates interannual variability. These results can contribute towards understanding climate and land-use change implications for environmental sustainability and for recommending climate adaptation strategies.

Droughts in the Amazon region have the potential to generate severe socio-environmental impacts in addition to having the ability to interfere with the long-term carbon cycle, thus affecting global climate. The 2015/2016 drought that occurred in this region, associated with an El Niño, was considered a record-breaking event in terms of unprecedented warming and the largest extent of the drought affected areas. Anthropogenic influence on the probability and intensity of this drought was assessed using two ensembles of the Met Office's HadGEM3-GA6 model. One ensemble was driven only with natural forcings and the other also included anthropogenic forcings. This analysis found that the intensity and probability of the 2015/2016 Amazon drought likely increased due to anthropogenic influence. The reliability of the model to represent the precipitation of the study area was assessed by comparing it with the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) product (R² = 0.81). Results indicate that anthropogenic forcings altered the drought intensity of 2015/2016 in the Amazon and increased the risk of this event by about four times with a confidence interval ranging from 2.7 to 4.7. We conclude that anthropogenic emissions threaten the functioning of the Amazon forest due to increased likelihood of extreme droughts.

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.