Climate Resilience and Sustainability最新文献

Southern Asia experiences some of the most damaging climate events in the world, with loss of life from some cyclones in the hundreds of thousands. Despite this, research on climate extremes in the region is substantially lacking compared to other parts of the world. To understand the narrative of how an extreme event in the region may change in the future, we consider Super Cyclone Amphan, which made landfall in May 2020, bringing storm surges of 2–4 m to coastlines of India and Bangladesh. Using the latest CMIP6 climate model projections, coupled with storm surge, hydrological, and socio-economic models, we consider how the population exposure to a storm surge of Amphan's scale changes in the future. We vary future sea level rise and population changes consistent with projections out to 2100, but keep other factors constant. Both India and Bangladesh will be negatively impacted, with India showing >200% increased exposure to extreme storm surge flooding (>3 m) under a high emissions scenario and Bangladesh showing an increase in exposure of >80% for low-level flooding (>0.1 m). It is only when we follow a low-emission scenario, consistent with the 2°C Paris Agreement Goal, that we see no real change in Bangladesh's storm surge exposure, mainly due to the population and climate signals cancelling each other out. For India, even with this low-emission scenario, increases in flood exposure are still substantial (>50%). While here we attribute only the storm surge flooding component of the event to climate change, we highlight that tropical cyclones are multifaceted, and damages are often an integration of physical and social components. We recommend that future climate risk assessments explicitly account for potential compounding factors.

Most studies into the effects of climate change have headline results in the form of a global change in mean temperature. More useful for businesses and governments, however, are measures of the localized impact, and also of extremes rather than averages. We have addressed this by examining the change in frequency of exceeding a daily mean temperature threshold, defined as ‘disruption days’, as it is often this exceedance which has the most dramatic impacts on personal or economic behaviour. Our exceedance analysis tackles the resolution of climate change both geographically and temporally, the latter specifically to address the 5- to 20-year time horizon which can be recognized in business planning.

We apply bias correction with quantile mapping to meteorological reanalysis data from ECMWF ERA5 and output from CMIP5 climate model simulations. By determining the daily frequency at which a mean temperature threshold is exceeded in this bias-corrected dataset, we can compare predicted and historic frequencies to estimate the change in the number of disruption days. Furthermore, by combining results from 18 different climate models, we can estimate the likelihood of more extreme events, taking into account model variations. This is useful for worst-case scenario planning.

Taking the city of Chicago as an example, the expected frequency of years with 40 or more disruption days above the 25°C threshold rises by a factor of four for a time period centred on 2040, compared with a period centred on 2000. Alternately, looking at the change in the number of days at a given likelihood, an example is Shenzhen, where the number of disruption days in a once-per-decade event exceeding the 25°C or 30°C threshold is expected to rise by a factor of four.

In a future stage, superimposing these results onto maps of, for instance, GDP sensitivity or production days lost, will provide more accurate and targeted conclusions for future impacts of climate change. This method of quantifying costs on business-relevant timescales will enable businesses and governments properly include risks associated with facilities, plan mitigating actions and make accurate provisions. It can also, for example, inform their disclosure of physical risks under the framework of the Task Force on Climate-related Financial Disclosures. This approach is equally applicable to other weather-related, localized phenomena likely to be impacted by climate change.

Brazil has experienced huge areas of forest loss over recent decades with an estimated removal of 80 MHa of natural forest since 1990. Deforestation creates substantial greenhouse emissions that have historically dominated all other sectors. Effective governance has reduced deforestation and net land-use emissions have fallen by 74% since the mid-2000s. Anthropogenic carbon removal from secondary forest regrowth and protected areas has increased by 62%, which has helped drive the reduction in net emissions, offsetting gross emissions which have fallen by 44%. Major Brazilian biomes, such as the Atlantic Forest are net-sinks and the Amazon was near net-zero in 2010. Deforestation has increased over the last 10-years and now stands at a decadal high in the Amazon region. These increases in deforestation put Brazil at risk of missing its original National Determined Contribution; however, the recent revision has substantially increased the 2005 baseline and therefore the overall target. Carbon removals in the forest sector play an increasingly important role in reducing emissions and achieving the NDC. The Brazilian target of achieving 12 MHa of reforestation and restoration has the potential to further offset emissions through enhanced regrowth. However, the natural carbon sinks of Brazil are weakening. The Amazon forest is the single largest Brazilian biome for natural carbon uptake but when combined with land-use emissions has seen a net loss over the last 30 years. The natural sink remains large, but ecosystem resilience is declining driven by global and local climate change linked to rising international emissions and changing circulation patterns associated with local deforestation and degradation. These combine to make realizing the huge potential for carbon removal more challenging. It remains evident that forest protection and avoided degradation and disturbance is the best way to mitigate emissions and reduce climate impacts.

Human activities continue to warm our climate in ways unprecedented in thousands of years. The latest Intergovernmental Panel on Climate Change (IPCC) report confirms our role in this, and that more frequent and extreme events and impacts are being felt right now in all areas of the world. It is clear that we must adapt to some level of changes already occurring whilst also reducing our emissions to limit further damage and unmanageable impacts. What this means for society requires understanding at local levels to aide decision-making. The Climate Science for Service Partnership Brazil (CSSP Brazil) supports collaborative research between Brazil and U.K. partners to improve climate resilience and sustainability with particular focus on Brazil but with underpinning capability applicable more widely.

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.