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A changing Amazon rainforest: Historical trends and future projections under post-Paris climate scenarios
Abstract
Despite the progress in sustainable development strategies, the role of the Amazon rainforest as a carbon sink faces increasing disturbances that may have a critical impact on global climate. Understanding the vulnerability of the Amazon rainforest to climate change is a major challenge, considering the complex interaction between human and natural systems. This paper aims, via an interdisciplinary approach, to assess the observed evolution and possible future of the Amazon rainforest, considering different global climate and socioeconomic scenarios. By comparing historical with plausible future developments, we present key knowledge to inform mitigation and regional adaptation policy considerations. As an entry point, historical trends of annual mean temperature and precipitation were analysed. In a second step, the same assessment was made for the mean annual NDVI sum (a proxy of yearly plant productivity), representing vegetation strength. For these purposes, a 34-year period (1982-2015) was considered. Trends were analysed based on non-parametric Mann-Kendall and Sen's methods. With this representation of the past, the next step focused on future scenarios. The most plausible global emission pathways were evaluated via the comparison of ten assessments of the possible effects of the mitigation action plans of national governments, as stated in the National Determined Contributions (NDCs). Results indicate a strong consensus that if either current policies, unconditional or conditional NDCs are fulfilled, the limit of global warming by "well below 2 • C" will be exceeded. In this context, climate projections for the Amazon suggest, among other results, an increase in the range of 1.3 • C (lower limit under SSP1-2.6) to 6.5 • C (upper limit under SSP5-8.5). Unlike temperature, positive and negative anomalies are expected for precipitation depending on location. Despite the uncertainty regarding the projections, possible changes such as forest diebacks and sav-annization may take place, namely in southeastern Amazon, by the end of the century. Overall, this study highlights the importance of carefully considering the combination of different factors, such as deforestation, to guarantee rainforest resilience under climate-driven changes.
... CRU datasets are commonly used as reference datasets in studies analyzing South American drought events (e.g. Nasrollahi et al., 2015;Carvalho et al. 2020;Rivera & Arnould 2020). ...
... As depicted in section 3.1, the CMIP6 models convey areas of weak spatial agreement with the instrumental ensemble. With the exception of the far west of the Basin which depicts homogenously low precipitation values, the observational data convey a general pattern of increased precipitation in Northwestern Amazonia (around 3000-3500 mm year -1 ) and decreased precipitation in the south, a finding consistent with recent studies (Espinoza et al., 2018;Carvalho et al., 2020). For one, analyzing the period 1982-2015, Carvalho et al. (2020) observed heightened precipitation in the northwest (~3000 mm year -1 ) and decreased precipitation in Western Amazonia. ...
... With the exception of the far west of the Basin which depicts homogenously low precipitation values, the observational data convey a general pattern of increased precipitation in Northwestern Amazonia (around 3000-3500 mm year -1 ) and decreased precipitation in the south, a finding consistent with recent studies (Espinoza et al., 2018;Carvalho et al., 2020). For one, analyzing the period 1982-2015, Carvalho et al. (2020) observed heightened precipitation in the northwest (~3000 mm year -1 ) and decreased precipitation in Western Amazonia. While the CMIP6 ensemble portrays a similar pattern of low precipitation in the south and increased precipitation in the northwest, the model ensemble conveys significantly low precipitation in the north-northeast of the Basin-a pattern inconsistent with observational precipitation patterns. ...
Deforestation, changes in seasonal climatology, and the resulting impacts on drought and forest fires coalesce into a strong positive feedback loop in the Amazon Basin, leading to the potential passing of tipping points beyond which the rainforest ecosystem may no longer be able to sustain itself. As the Amazon Rainforest plays a vital role in both global and regional biogeochemical and hydrological cycles, assessing climatic drivers and future projections of Amazonian drought is imperative to better understand current and future climatic changes. In this regard, the present study seeks to evaluate the newest iteration of climate models included in the Climate Model Intercomparison Project (CMIP6) in their representation of current as well as future projections of drought under a high and low emissions scenario. The results suggest a continuation of persistent biases in CMIP6 models identified in previous iterations, such as an underestimation of precipitation, poor representation of variable topographic regions, and an inability to accurately represent long-term precipitation trends and inter-annual variability. Moreover, consistent with existing analyses, CMIP6 model projections reveal an overall drying trend over the 21st century, with Eastern and Southern Amazonia most susceptible to decreased precipitation and Southwestern Amazonia most vulnerable to accumulated water stress. Significant reductions in precipitation during dry-to-wet season transition months is projected to lengthen the Amazonian dry season, which may have serious ramifications for fire risk and ecosystem resilience. Simulated drought events occurring at the end of the 21st century convey similarities to current ‘one in a century droughts’, revealing an accumulation of drought stress throughout dry and dry-to-wet transition seasons as well as an increased frequency of the spatial extent of current major droughts.
... To this end, we applied the non-parametric Mann-Kendall trend test [45,46], which enables the determination of whether a trend exists in a time series. This test has been successfully applied in similar studies [47][48][49][50]. The Mann-Kendall test was applied to all of the 31 ILs of the BLA where mining activity was detected in at least one of 36 years analyzed. ...
Conserving tropical forests is crucial for the environment and future of our climate. Tropical
rainforests worldwide, including the Brazilian Legal Amazon (BLA), offer exceptional ecosystem
services. However, the disturbances that have been occurring more frequently within them are endangering
their key role in tackling climate change. An alternative approach for preserving the intact
forests that remain in the BLA is the delimitation of Indigenous Lands (ILs), which can, additionally,
ensure the well-being of the traditional peoples inhabiting there. An increase in deforestation rates of
the BLA in recent years, due to the weakening of the Brazilian environmental policy, is not confined
to unprotected areas but is also occurring within ILs. Under this scenario, mining, not allowed in ILs,
is a growing threat in these protected areas. Thus, using the freely available MapBiomas dataset, we
have quantified for the first time the total mining area within ILs of the BLA from 1985 to 2020. Such
activity jumped from 7.45 km2 in 1985 to 102.16 km2 in 2020, an alarming increase of 1271%. Three
ILs (Kayapó, Mundurukú, and Yanomami) concentrated 95% of the mining activity within ILs in 2020
and, therefore, they require closer monitoring. Most of the mining in ILs in 2020 (99.5%) was related
to gold extraction. A total of 25 of the 31 ILs of the BLA where mining activity was detected in at least
one of 36 years analyzed (~81% of them) had a statistically significant increasing trend according to
the Mann–Kendall test at 5%. The datasets used or cited in this study (MapBiomas, PRODES, and
DETER) enable the monitoring of the current status of ILs, and the identification of emerging trends
related to illegal activities. Therefore, they are critical tools for legal authorities.
... Tropical forest ecosystems often host biodiversity hotspots, particularly threatened by habitat loss and degradation (Myers et al., 2000;Negret et al., 2021). Such an accelerated forest loss in the tropics is putting biodiversity (Martins et al., 2021) and ecosystem services at stake (Navarrete et al., 2016;UICN, 2017;Carvalho et al., 2020;Ruiz-Agudelo et al., 2020). ...
The Amazon basin is one of the most extensive, biodiverse, and dynamic tropical forest ecosystems on the earth. The Colombian Amazon basin occupies an area of approx. 34 million hectares, located in the country's southeast. The literature about the economic valuation of ecosystem services (ES) and the spatial information on remnant natural resources in the Colombian Amazon basin was revised through various information sources to document the earliest approximation to the state, spatial distribution, and economic value of the remnant natural capital at the scale of biomes, specific ecosystems, and political-administrative units. Our assessment estimated a natural capital loss of 18.1 billion $/year (equivalent to 6.7% of Colombian GDP in 2020) and a remnant natural capital worth 153.9 billion $/year (57 % of Colombia's GDP in 2020) for eight ecosystem services. This research founds that a potential expansion in extensive and intensive livestock production systems (in a ten-year projection) will generate an additional loss of remnant natural capital of approximately 9.1 billion $/year. Finally, considering that 63% of the remnant natural capital is represented in indigenous reservations and 28% in protected areas, it is essential that the political management of the Amazon Basin concentrate on strengthening these land management figures. Improving the governability of indigenous lands and incentive sustainable agricultural and cattle ranching production, are the principal political challenges.
... Although there is an increasing number of studies analyzing ecological regime shifts using time series records, ecological alternative states identified in these studies are mainly based on the turning points of a single ecological component, instead of the interactions between them (Wu et al., 2020). In particular, remotely sensed vegetation dynamics, commonly represented by time series of normalized difference vegetation index (NDVI), leaf area index (LAI), and net primary production (NPP), have been widely used to understand trends in land use cover change (LUCC) and ecosystem shifts at regional and/or global level (e.g., Bendor, 2009;Carvalho et al., 2020;Chen et al., 2019;De Beurs, et al., 2015;Fensholt et al., 2012;Jong et al., 2012;Pan et al., 2018). However, because sparse vegetation is interspersed by a heterogeneous bare ground matrix in drylands (Aguiar and Sala, 1999;Schmitz, 2010), the low-sensitivity of vegetation indices limits the changes that can be detected at a given specific remote sensing scale (Gilabert et al., 2002;Huete, 1988). ...
Understanding and early identification of state transitions in dryland systems’ landscape pattern are challenging but critical for conservation planning and sustainable management strategies. Currently the alternative states of drylands have been identified based on dynamics of single vegetation index (i.e., normalized difference vegetation index), with the interactions between vegetation and soil components often being neglected. Here, we proposed a framework for identifying the alternative states in relation to land degradation based on the interactive pathways of fractional vegetation and soil-related endmember nexuses. In this framework, we initially generated 16-day fractional green vegetation endmember (green vegetation [GV]) and soil-related endmembers (sand [SL], saline [SA], dark surface [DA]) time series using a standardized linear spectral mixture analysis model in the Hexi Corridor during 2001–2017. A discrete wavelet transform based trends and turning points detection (DWT-TTD) algorithm was proposed to detect trends, turning point types, and timing of these time series records. These metrics were subsequently used to identify eight state transition pathways for each endmember and synthetic degradation processes at the appropriate 5–10 year scale. We found that the unmixed fractional endmembers exhibited a consistent agreement with both field abundance values and estimated fractions from high-spatial-resolution Google Earth images. Based on the DWT-TTD algorithm derived alternative pathways, we found that four positive pathways of GV and DA fractions can be observed in the vegetated areas were integrated with corresponding negative pathways of SA and SL fractions. These pathways resulted in remarkably increasing GV (+4551.81 km²) and DA (+9749.26 km²) and significantly declining SA (−1452.70 km²) and SL (−9555.57 km²) from 2001 to 2017. These processes were linked to increased precipitation and temperature and reduced drought degree. Also, the timing of the turning points (around 2007 and 2013) was largely coincidental with the timing of ecological management, suggesting that ecological governance played a crucial role in the state shifts. However, because of drought trends in the barren regions, land desertification exhibited an increasing occurrence in extent (+2.5% of total pixels) during the study period, which may increase in the future, as indicated by the large pixel proportion of initially positive reversal pathway for SL (9.31%). Compared to state-of-the-art approaches, the DWT-TTD algorithm, as a multi-resolution analysis method, has the potential to detect hierarchical temporal changes and disentangle the cross-scale inherent shifts of vegetation-soil systems. The framework can help devise sustainable management practices that maintain land degradation neutrality, especially when integrated with land use cover change analysis.
Since 2012, deforestation rates have been increasing within the Brazilian Amazon. This situation undermines the mitigation efforts against global climate change and stresses our need for both accurate identification of deforestation hotspots and promotion of law enforcement. It also implies a permanent need to explore the deforestation data, looking for patterns that enable the public, the academia, and authorities to anticipate and prepare for deforestation outburst. For this reason, we analyzed data of the Brazilian deforestation monitoring program (PRODES) to find deforestation patterns within Full Protected Areas of the Brazilian Amazon. Our aim is to identify and quantify deforestation from 2008 to 2021. We applied a trend analysis to find FPAs with significant increments and we found that approximately 15% of FPAs have statistically significant deforestation growth according to the Mann-Kendall test at 5% significance. Furthermore, we found that only four FPAs accounted for 67.6% of the deforestation increments during 2021, making them good candidates for closer monitoring and law enforcement. Besides, our results also show the role of FPAs as forest safeguards, although deforestation within them is increasing.
Droughts are one of the most devastating natural disasters. Droughts can co-exist in different forms (e.g. meteorological, hydrological, and agricultural) as concurrent droughts. Such concurrent droughts can have far reaching implications for crop yield and global food security. The present study aims to assess global concurrent drought traits and their effects on maize yield under climate change. The standardized indices of precipitation, runoff, and soil moisture incorporated as multivariate standardized drought index (MSDI) using copula functions are used to quantify the concurrent droughts. The ensemble data of several General Circulation Models (GCMs) considering the high emission scenario of Coupled Model Intercomparison Project phase 6 (CMIP6) are utilized. Applying run theory on a time series (1950–2100) of MSDI values, the duration, severity, areal coverage, and average areal intensity of concurrent droughts are computed. The temporal evolution of drought duration and severity are compared among historical (1950–2014), near future (2021–2060), and far future (2061–2100) timeframes. The results indicate that the most vulnerable regions in the late 21st century are Central America, the Mediterranean, Southern Africa, and the Amazon basin. The indices and spatial extent of the individual droughts are used as predictor variables to predict the country-level crop index of the top seven producers of maize. The historical dynamics between maize yield and different drought forms are projected using XGBoost (Extreme Gradient Boosting) algorithms. The future temporal changes in drought-crop yield dynamics are tracked using probabilities of various drought forms under yield-loss conditions. The conditional concurrent drought probabilities are as high as 84 %, 64 %, and 37 % in France, Mexico, and Brazil, revealing that concurrent drought affects the maize yield tremendously in the far future. This approach of applying statistical and soft-computing techniques could aid in drought mitigation under changing climatic conditions.
The Amazon is rapidly approaching its tipping point, which could turn a once enchanted tropical rainforest into a dry, carbon-emitting savannah. This will have catastrophic impacts well beyond the South-American continent and its inhabitants. The region is facing a nowadays familiar challenge of combating climate change and promoting social justice. International climate governance is proving ineffective , as it fails to incorporate the long term wellbeing of local communities. Demands for justice have led to calls for more polycentric climate governance. This approach aims to provide a culture-specific and place-based approach to dealing with the possible consequences of climate change for social justice and sustainable livelihoods. This article examines the scope for introducing Intercultural Polycentric Climate Governance (IPCG) to the Amazon. We select two examples of subnational climate governance and indigenous peoples' participation in the Amazon as our case studies: the State of Acre in Brazil and the regional department of Ucayali in Peru. Both are seen as pioneers of intercultural climate governance in their national contexts, and both have established indigenous working groups geared to promote the provision of intercultural fairness within their regional governance mechanisms. We conducted a qualitative content analysis, both of our interviews and relevant policy documents. Our study highlights three challenges for successful IPCG: 1) overcoming intercultural injustices; 2) increasing meaningful participation; and 3) filling governance gaps. Our findings reveal that there is still some way to go to meet these outcomes. Bridging polycentricity and interculturality, diverse systems of knowledge and their adherents need to be better appreciated and incorporated as part of the process of reassessing the purpose of IPCG. Only then, will we see the handling of the future of the enchanting Amazon in a holistic way: so much more than mere carbon storage.
The Legal Amazon was established by the Brazilian government in 1953, in order to plan and provide for the social and economic development of the Amazon region. This is a region of great importance due to the maintenance of biogeochemical cycles that the forest exercises and for being a biodiversity hotspot. Amazon has been suffering from an intense and rapid change in land use and coverage, where deforestation and forest fragmentation stem from the agriculture expansion and wood exploitation. Therefore, this work aimed to identify and measure changes in the hydrological cycle and in the dynamics of climatic elements caused by human activities in the Legal Amazon. In addition, predictions of land use and land cover conditions were made for the years 2040, 2070 and 2099 allowing the knowledge of the level of long-term deforestation in this area. To achieve the objective, data from the study area were collected and standardized. The data was processed and compared, using the Land Change Modeler and Earth Trends Modeler modules. The results obtained by the modules are interrelated. Hydrological and climatic variables are impacted by changes in land use and coverage, from the expansion anthropic activities over natural vegetation, mainly in the area defined in this study, where the direct impact on NDVI is evident. These results corroborate to show that, in the legal Amazon, the forest has a direct influence on the climate. In this context, the failure to adopt conservationist practices and contain the natural forest suppression, increases deforestation, causing changes in the variables studied, tending to a worsening of climatic conditions.
Within the Copernicus Climate Change Service (C3S), ECMWF is producing the ERA5 reanalysis, which embodies a detailed record of the global atmosphere, land surface and ocean waves from 1950 onwards once completed. This new reanalysis replaces the ERA‐Interim reanalysis that was started in 2006 (spanning 1979 onwards). ERA5 is based on the Integrated Forecasting System (IFS) Cy41r2 which was operational in 2016. ERA5 thus benefits from a decade of developments in model physics, core dynamics and data assimilation. In addition to a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA‐Interim, ERA5 has hourly output throughout, and an uncertainty estimate from an ensemble (3‐hourly at half the horizontal resolution). This paper describes the general setup of ERA5, as well as a basic evaluation of characteristics and performance. Focus is on the dataset from 1979 onwards that is currently publicly available. Re‐forecasts from ERA5 analyses show a gain of up to one day in skill with respect to ERA‐Interim. Comparison with radiosonde and PILOT data prior to assimilation shows an improved fit for temperature, wind and humidity in the troposphere, but not the stratosphere. A comparison with independent buoy data shows a much improved fit for ocean wave height. The uncertainty estimate reflects the evolution of the observing systems used in ERA5. The enhanced temporal and spatial resolution allows for a detailed evolution of weather systems. For precipitation, global‐mean correlation with monthly‐mean GPCP data is increased from 67% to 77%. In general low‐frequency variability is found to be well‐represented and from 10 hPa downwards general patterns of anomalies in temperature match those from the ERA‐Interim, MERRA‐2 and JRA‐55 reanalyses. This article is protected by copyright. All rights reserved.
Spatial and temporal patterns of rainfall are governed by complex interactions between climate and landscape perturbations including deforestation, fire, and drought. Previous research demonstrated that rainfall in portions of the Amazon Basin has intensified, resulting in more extreme droughts and floods. The basin has global impacts on climate and hydrologic cycles; thus, it is critical to understand how precipitation patterns and intensity are changing. Due to insufficient precipitation gauges, we analyzed the variability and seasonality of rainfall over the Amazon Basin from 1982 to 2018 using high-resolution gridded precipitation products. We developed several precipitation indices and analyzed their trends using the Mann–Kendall test (Mann 1945; Kendall, 1975) to identify significant changes in rainfall patterns over time and space. Our results show landscape scale changes in the timing and intensity of rainfall events. Specifically, wet areas of the western Basin have become significantly wetter since 1982, with an increase of 182 mm of rainfall per year. In the eastern and southern regions, where deforestation is widespread, a significant drying trend is evident. Additionally, local alterations to precipitation patterns were also observed. For example, the Tocantins region has had a significant increase in the number of dry days during both wet and dry seasons, increasing by about 1 day per year. Surprisingly, changes in rainfall amount and number of dry days do not consistently align. Broadly, over this 37-year period, wet areas are trending wetter and dry areas are trending drier, while spatial anomalies show structure at the scale of hundreds of kilometers.
The influence of global change on vegetation cover and processes has drawn increasing attention in the past few decades. In this study,
we used remotely sensed rainfall and land surface temperature to investigate the spatiotemporal pattern and trend in vegetation
condition using NDVI as proxy from 2001 to 2017 in a humid and dry tropical region. We also determined the partial correlation
coefficient of temperature and rainfall with NDVI and the response of NDVI to changes in landcover categories due to human activities.
We found that the mean annual maximum NDVI was 0.42, decreasing at a rate of 0.06 per decade. About 27.4% of the area was found to
have experienced a significant negative trend in vegetation cover, while only 0.34% exhibited significant increasing vegetation vigour.
Land surface temperature increased at a mean rate of 0.75°
C/decade, with higher rates in agriculture, savanna, settlements, woodlands,
and riparian vegetation than in forest and mangrove vegetations. Precipitation also reduced at a mean rate of 58.69 mm/decade, with
higher rates in agriculture savanna and riparian vegetation than in sahelian grasslands, mangrove, forest, and woodlands. NDVI was
negatively correlated with temperature in savanna, settlements, degraded forest, and sahelian grasslands providing confirmation of
ongoing land degradation. It was concluded that vegetation vigour will continue to decline under reducing rainfall and increasing temperature
conditions especially in dryer regions. The use of land surface temperature in this study is particularly valuable in highlighting areas
where changes in NDVI occurred as a result of synergistic action between climate and human-induced landcover changes. Our findings
underscore the importance of landuse policies that account for spatial variation in synergistic relationships between the nexus of climate
and land conversion processes that influence vegetation cover change in different landcover types in tropical regions.
Anthropogenic increases of atmospheric greenhouse gas concentrations are the main driver of current and future climate change. The Integrated Assessment community quantified anthropogenic emissions for the Shared Socioeconomic Pathways (SSP) scenarios, each of which represents a different future socio-economic projection and political environment. Here, we provide the greenhouse gas concentration for these SSP scenarios – using the reduced complexity climate-carbon cycle model MAGICC7.0. We extend historical, observationally-based concentration data with SSP concentration projections from 2015 to 2500 for 43 greenhouse gases with monthly and latitudinal resolution. CO2 concentrations by 2100 range from 393 to 1135 ppm for the lowest (SSP1-1.9) and highest (SSP5-8.5) emission scenarios respectively. We also provide the concentration extensions beyond 2100 based on assumptions in the trajectories of fossil fuels and land use change emissions, net negative emissions, and the fraction of non-CO2 emissions. By 2150, CO2 concentrations in the lowest emission scenario are approximately 350 ppm and approximately plateau at that level until 2500, whereas the highest fossil-fuel driven scenario projects CO2 concentrations of 1737 ppm and reaches concentrations beyond 2000 ppm by 2250. We estimate that the share of CO2 in the total radiative forcing contribution of all considered 43 long-lived greenhouse gases increases from today 66 % to roughly 68 % to 85 % by the time of maximum forcing in the 21st century. For this estimation, we updated simple radiative forcing parameterisations that reflect the Oslo Line by Line model results. In comparison to the RCPs, the five main SSPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) are more evenly spaced in terms of their expected global-mean temperatures, extend to lower 2100 temperatures and sea level rise than the RCP set. Performing 2 pairs of 6-member historical ensembles with CESM1.2.2, we estimate the effect on surface air temperatures of applying latitudinally and seasonally resolved GHG concentrations. We find that the ensemble differences in the MAM season provide a regional warming in higher northern latitudes of up to 0.4 K over the historical period, latitudinally averaged of about 0.1 K, which we estimate to be comparable to the upper bound (∼ 5 % level) of natural variability. In comparison to the comparatively straight line of the last 2000 years, the greenhouse gas concentrations since the onset of the industrial period and this studies’ projections over the next 100 to 500 years unequivocally depict a ‘hockey-stick’ upwards shape – it is a collective choice whether the hothouse pathway is pursued or whether we manage climate damages to the SSP1-1.9 equivalent of around 1.5 °C warming.
Normalized difference vegetation index (NDVI) based models have been developed to derive wheat grain yields with multispectral images. In this regard, field measurements and Landsat 8 Operational Land Imager (OLI) data were used for two growing seasons to determine the relationships between NDVI and yields. The number of six statistic parameters were calculated from NDVI values to find the best agreement with actual yield data. A comparison of the results showed that sum-NDVI better matched field measurements. To compare the results of NDVI with other vegetation indices, we applied four other vegetation indices. Results indicated that estimation of wheat yields using sum-NDVI values was more accurate than estimation by sum of the four applied vegetation indices values. Also, the investigation of multi-temporal images showed that the critical time to estimate wheat yield using sum-NDVI values was the time that wheat grains were in the milky and maturity stages.
We present a suite of nine scenarios of future emissions trajectories of anthropogenic sources, a key deliverable of the ScenarioMIP experiment within CMIP6. Integrated assessment model results for 14 different emissions species and 13 emissions sectors are provided for each scenario with consistent transitions from the historical data used in CMIP6 to future trajectories using automated harmonization before being downscaled to provide higher emissions source spatial detail. We find that the scenarios span a wide range of end-of-century radiative forcing values, thus making this set of scenarios ideal for exploring a variety of warming pathways. The set of scenarios is bounded on the low end by a 1.9 W m⁻² scenario, ideal for analyzing a world with end-of-century temperatures well below 2 ∘C, and on the high end by a 8.5 W m⁻² scenario, resulting in an increase in warming of nearly 5 ∘C over pre-industrial levels. Between these two extremes, scenarios are provided such that differences between forcing outcomes provide statistically significant regional temperature outcomes to maximize their usefulness for downstream experiments within CMIP6. A wide range of scenario data products are provided for the CMIP6 scientific community including global, regional, and gridded emissions datasets.
We present new global maps of the Köppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day (1980–2016) and for projected future conditions (2071–2100) under climate change. The present-day map is derived from an ensemble of four high-resolution, topographically-corrected climatic maps. The future map is derived from an ensemble of 32 climate model projections (scenario RCP8.5), by superimposing the projected climate change anomaly on the baseline high-resolution climatic maps. For both time periods we calculate confidence levels from the ensemble spread, providing valuable indications of the reliability of the classifications. The new maps exhibit a higher classification accuracy and substantially more detail than previous maps, particularly in regions with sharp spatial or elevation gradients. We anticipate the new maps will be useful for numerous applications, including species and vegetation distribution modeling. The new maps including the associated confidence maps are freely available via www.gloh2o.org/koppen.
In the Paris Agreement countries have agreed to act together to hold global warming well below 2°C over preindustrial levels and to pursue efforts to limit warming to 1.5°C. To assess if the world is on track to meet this long‐term temperature goal, countries' pledged emissions reductions (Nationally Determined Contributions, NDCs) need to be analyzed for their implied warming. Several research groups and nongovernmental organizations have estimated this warming and arrived at very different results but have invariably concluded that the current pledges are inadequate to hold warming below 2°C, let alone 1.5°C. In this paper we analyze different methods to estimate 2100 global mean temperature rise implied by countries' NDCs, which often only specify commitments until 2030. We present different methods to extend near‐term emissions pathways that have been developed by the authors or used by different research groups and nongovernmental organizations to estimate 21st century warming consequences of Paris Agreement commitments. The abilities of these methods to project both low and high warming scenarios in line with the scenario literature is assessed. We find that the simpler methods are not suitable for temperature projections while more complex methods can produce results consistent with the energy and economic scenario literature. We further find that some methods can have a strong high or low temperature bias depending on parameter choices. The choice of methods to evaluate the consistency of aggregated NDC commitments is very important for reviewing progress toward the Paris Agreement's long‐term temperature goal.
Amazon forests have experienced frequent and severe droughts in the past two decades. However, little is known about the large-scale legacy of droughts on carbon stocks and dynamics of forests. Using systematic sampling of forest structure measured by LiDAR waveforms from 2003 to 2008, here we show a significant loss of carbon over the entire Amazon basin at a rate of 0.3 ± 0.2 (95% CI) PgC yr-1 after the 2005 mega-drought, which continued persistently over the next 3 years (2005-2008). The changes in forest structure, captured by average LiDAR forest height and converted to above ground biomass carbon density, show an average loss of 2.35 ± 1.80 MgC ha-1 a year after (2006) in the epicenter of the drought. With more frequent droughts expected in future, forests of Amazon may lose their role as a robust sink of carbon, leading to a significant positive climate feedback and exacerbating warming trends.
More than ten years after REDD + (Reducing Emissions from Deforestation and forest Degradation) entered the UN climate negotiations, its current state and future direction are a matter of contention. This paper analyses 162 INDCs (Intended National Determined Contributions), or climate action plans, to assess whether and how countries plan to use REDD + in their implementation of the Paris Agreement. Our analysis suggests that REDD + continues to have political traction. Many tropical countries still have expectations of REDD +, and hope that public and private donors will support chronically underfunded domestic conservation programs. However, the expectations are not formulated in detail. We argue that until the questions of how to finance REDD + and how to deal with the drivers of deforestation are resolved, REDD is unlikely to move quickly from formulated INDCs plans to implementation on the ground.
Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity from being estimated over large areas.We used a field-based time-series (2000−2009) of live aboveground vascular plant biomass data and a recently processed cloud-free MODIS-NDVI data set (2000−2014) to estimate, on a pixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring to the average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensed productivity measure was then compared, at two different spatial scales, with the peak plant biomass field data. At both the local scale, surrounding the field data site, and the larger regional scale, our NDVI measure was found to predict plant biomass (adjusted R2 = 0.51 and 0.44, respectively). The commonly used ‘maximum NDVI’ plant productivity index showed no relationship with plant biomass, likely due to some years having very few cloud-free images available during the peak plant growing season. Thus, we propose this new summed NDVI from onset of spring to time of peak NDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climatic conditions restrict the availability of cloud-free images.
Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources.
We created a new dataset of spatially interpolated monthly climate data for global land areas at a very high spatial resolution (approximately 1 km 2). We included monthly temperature (minimum, maximum and average), precipitation, solar radiation, vapour pressure and wind speed, aggregated across a target temporal range of 1970–2000, using data from between 9000 and 60 000 weather stations. Weather station data were interpolated using thin-plate splines with covariates including elevation, distance to the coast and three satellite-derived covariates: maximum and minimum land surface temperature as well as cloud cover, obtained with the MODIS satellite platform. Interpolation was done for 23 regions of varying size depending on station density. Satellite data improved prediction accuracy for temperature variables 5–15% (0.07–0.17 ∘ C), particularly for areas with a low station density, although prediction error remained high in such regions for all climate variables. Contributions of satellite covariates were mostly negligible for the other variables, although their importance varied by region. In contrast to the common approach to use a single model formulation for the entire world, we constructed the final product by selecting the best performing model for each region and variable. Global cross-validation correlations were ≥ 0.99 for temperature and humidity, 0.86 for precipitation and 0.76 for wind speed. The fact that most of our climate surface estimates were only marginally improved by use of satellite covariates highlights the importance having a dense, high-quality network of climate station data.
Along with global climate change, the occurrence of extreme droughts in recent years has had a serious impact on the Amazon region. Current studies on the driving factors of the 2005 and 2010 Amazon droughts has focused on the influence of precipitation, whereas the impacts of temperature and radiation have received less attention. This study aims to explore the climate-driven factors of Amazonian vegetation decline during the extreme droughts using vegetation index, precipitation, temperature and radiation datasets. First, time-lag effects of Amazonian vegetation responses to precipitation, radiation and temperature were analyzed. Then, a multiple linear regression model was established to estimate the contributions of climatic factors to vegetation greenness, from which the dominant climate-driving factors were determined. Finally, the climate-driven factors of Amazonian vegetation greenness decline during the 2005 and 2010 extreme droughts were explored. The results showed that (i) in the Amazon vegetation greenness responded to precipitation, radiation and temperature, with apparent time lags for most averaging interval periods associated with vegetation index responses of 0–4, 0–9 and 0–6 months, respectively; (ii) on average, the three climatic factors without time lags explained 27.28±21.73% (mean±1 SD) of vegetation index variation in the Amazon basin, and this value increased by 12.22% and reached 39.50±27.85% when time lags were considered; (iii) vegetation greenness in this region in non-drought years was primarily affected by precipitation and shortwave radiation, and these two factors altogether accounted for 93.47% of the total explanation; and (iv) in the common epicenter of the two droughts, pixels with a significant variation in precipitation, radiation and temperature accounted for 36.68%, 40.07% and 10.40%, respectively, of all pixels showing a significant decrease in vegetation index in 2005, and 15.69%, 2.01% and 45.25% in 2010, respectively. Overall, vegetation greenness declines during the 2005 and 2010 extreme droughts were adversely influenced by precipitation, radiation and temperature; this study provides evidence of the influence of multiple climatic factors on vegetation during the 2005 and 2010 Amazon droughts.
The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS.
The executive and legislative branches of Brazilian government have either proposed or taken a variety of initiatives that threaten biodiversity and ecosystems. Opposition by the scientific community has largely been ignored by decision-makers. In this short essay, we present recent examples of harmful policies that have great potential to erode biodiversity, and we suggest ways to communicate scientific knowledge to decision- makers. If the current gap between conservation science and policies is not filled, the country will threaten the maintenance of its natural capital and, consequently, the sustainability of essential societal activities in the long term.
Remote sensing supports carbon estimation, allowing the upscaling of field measurements to large extents. Lidar is considered the premier instrument to estimate above ground biomass, but data are expensive and collected on-demand, with limited spatial and temporal coverage. The previous JERS and ALOS SAR satellites data were extensively employed to model forest biomass, with literature suggesting signal saturation at low-moderate biomass values, and an influence of plot size on estimates accuracy. The ALOS2 continuity mission since May 2014 produces data with improved features with respect to the former ALOS, such as increased spatial resolution and reduced revisit time. We used ALOS2 backscatter data, testing also the integration with additional features (SAR textures and NDVI from Landsat 8 data) together with ground truth, to model and map above ground biomass in two mixed forest sites: Tahoe (California) and Asiago (Alps). While texture was useful to improve the model performance, the best model was obtained using joined SAR and NDVI (R² equal to 0.66). In this model, only a slight saturation was observed, at higher levels than what usually reported in literature for SAR; the trend requires further investigation but the model confirmed the complementarity of optical and SAR datatypes. For comparison purposes, we also generated a biomass map for Asiago using lidar data, and considered a previous lidar-based study for Tahoe; in these areas, the observed R² were 0.92 for Tahoe and 0.75 for Asiago, respectively. The quantitative comparison of the carbon stocks obtained with the two methods allows discussion of sensor suitability. The range of local variation captured by lidar is higher than those by SAR and NDVI, with the latter showing overestimation. However, this overestimation is very limited for one of the study areas, suggesting that when the purpose is the overall quantification of the stored carbon, especially in areas with high carbon density, satellite data with lower cost and broad coverage can be as effective as lidar.
Projections of future climate change play a fundamental role in
improving understanding of the climate system as well as characterizing
societal risks and response options. The Scenario Model Intercomparison
Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate
projections based on alternative scenarios of future emissions and land use
changes produced with integrated assessment models. In this paper, we
describe ScenarioMIP's objectives, experimental design, and its relation to
other activities within CMIP6. The ScenarioMIP design is one component of a
larger scenario process that aims to facilitate a wide range of integrated
studies across the climate science, integrated assessment modeling, and
impacts, adaptation, and vulnerability communities, and will form an
important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the
same time, it will provide the basis for investigating a number of targeted
science and policy questions that are especially relevant to scenario-based
analysis, including the role of specific forcings such as land use and
aerosols, the effect of a peak and decline in forcing, the consequences of
scenarios that limit warming to below 2 °C, the relative
contributions to uncertainty from scenarios, climate models, and internal
variability, and long-term climate system outcomes beyond the 21st
century. To serve this wide range of scientific communities and address
these questions, a design has been identified consisting of eight
alternative 21st century scenarios plus one large initial condition
ensemble and a set of long-term extensions, divided into two tiers defined
by relative priority. Some of these scenarios will also provide a basis for
variants planned to be run in other CMIP6-Endorsed MIPs to investigate
questions related to specific forcings. Harmonized, spatially explicit
emissions and land use scenarios generated with integrated assessment models
will be provided to participating climate modeling groups by late 2016, with
the climate model simulations run within the 2017–2018 time frame, and
output from the climate model projections made available and analyses
performed over the 2018–2020 period.
Targets are widely employed in environmental governance. In this paper, we investigate the construction of the 2 °C climate target, one of the best known targets in global environmental governance. Our paper examines this target through a historical reconstruction that identifies four different phases: framing, consolidation and diffusion, adoption, and disembeddedness. Our analysis shows that, initially, the target was science-driven and predominantly EU-based; it then became progressively accepted at the international level, despite a lack of broader debate among governments on the policy implications and required measures for implementation. Once the 2 °C target was endorsed at the level of the United Nations, the nature of the target changed from being policy-prescriptive to being largely symbolic. In this phase, the target became a disembedded object in global governance not linked to a shared agenda nor to coordinated and mutually binding mitigation efforts. The 2015 Paris Agreement marks the last stage in this development and may have further solidified the target as a disembedded object. In the final part of the paper, we suggest ways to overcome the current situation and to develop the 2 °C target into a fully fledged global environmental governance target.
Significance
The Amazonian tropical forests have been disappearing at a fast rate in the last 50 y due to deforestation to open areas for agriculture, posing high risks of irreversible changes to biodiversity and ecosystems. Climate change poses additional risks to the stability of the forests. Studies suggest “tipping points” not to be transgressed: 4° C of global warming or 40% of total deforested area. The regional development debate has focused on attempting to reconcile maximizing conservation with intensification of traditional agriculture. Large reductions of deforestation in the last decade open up opportunities for an alternative model based on seeing the Amazon as a global public good of biological assets for the creation of high-value products and ecosystem services.
This study analyzes the spatiotemporal variability of rainfall and temperature (minimum, maximum and average) trends at 47 stations throughout the Brazilian Legal Amazon for the period 1973–2013. Annual, wet season and dry season trends were quantified by Sen's slope for each station and the entire region. The Mann–Kendall test was used to determine the statistical significance of the trends. For the whole region, minimum, maximum and average annual temperatures showed increasing trend of approximately 0.04 °C per year. The rainfall showed an insignificant trend for most stations for annual and seasonal series. Nevertheless, some stations showed significant increasing trends in the annual and wet season rainfalls while a few stations showed decreasing trends for the dry season rainfall. A positive trend of the annual range between wet and dry season rainfall was found in some stations, caused mainly by an increasing trend in wet season rainfall.
This article deals with the spatio-statistical analysis of temperature trend using Mann–Kendall trend model (MKTM) and Sen’s slope estimator (SSE) in the eastern Hindu Kush, north Pakistan. The climate change has a strong relationship with the trend in temperature and resultant changes in rainfall pattern and river discharge. In the present study, temperature is selected as a meteorological parameter for trend analysis and slope magnitude. In order to achieve objectives of the study, temperature data was collected from Pakistan Meteorological Department for all the seven meteorological stations that falls in the eastern Hindu Kush region. The temperature data were analysed and simulated using MKTM, whereas for the determination of temperature trend and slope magnitude SSE method have been applied to exhibit the type of fluctuations. The analysis reveals that a positive (increasing) trend in mean maximum temperature has been detected for Chitral, Dir and Saidu Sharif met stations, whereas, negative (decreasing) trend in mean minimum temperature has been recorded for met station Saidu Sharif and Timergara. The analysis further reveals that the concern variation in temperature trend and slope magnitude is attributed to climate change phenomenon in the region.
We present a historical overview of forest concepts and definitions, linking these changes with distinct perspectives and management objectives. Policies dealing with a broad range of forest issues are often based on definitions created for the purpose of assessing global forest stocks, which do not distinguish between natural and planted forests or reforests, and which have not proved useful in assessing national and global rates of forest regrowth and restoration. Implementing and monitoring forest and landscape restoration requires additional approaches to defining and assessing forests that reveal the qualities and trajectories of forest patches in a spatially and temporally dynamic landscape matrix. New technologies and participatory assessment of forest states and trajectories offer the potential to operationalize such definitions. Purpose-built and contextualized definitions are needed to support policies that successfully protect, sustain, and regrow forests at national and global scales. We provide a framework to illustrate how different management objectives drive the relative importance of different aspects of forest state, dynamics, and landscape context.
Electronic supplementary material
The online version of this article (doi:10.1007/s13280-016-0772-y) contains supplementary material, which is available to authorized users.
Recent analyses of Amazon runoff and gridded precipitation data suggest an intensification of the hydrological cycle over the past few decades in the following sense: wet-season precipitation and peak river runoff (since ∼ 1980) as well as annual-mean precipitation (since ∼ 1990) have increased while dry-season precipitation and minimum runoff have slightly decreased. There has also been an increase in the frequency of anomalously severe floods and droughts. Here we extend and expand these analyses to characterize recent climate state and change, as a background for possible ongoing and future changes of these forests. The contrasting recent changes in wet and dry season precipitation have continued and are generally consistent with changes in catchment-level peak and minimum river runoff as well as a positive trend of water vapour inflow into the basin. Consistent with the river records the increased vapour inflow is concentrated to the wet season. Temperature has been rising by 0.7∘C since 1980 with more pronounced warming during dry months. Suggestions for the cause of the observed changes of the hydrological cycle come from patterns in tropical sea surface temperatures (SST's). Tropical and North Atlantic SST's have increased rapidly and steadily since 1990, while Pacific SST's have shifted from a negative Pacific Decadal Oscillation (PDO) phase (approximately pre 1990) with warm eastern Pacific temperatures to a positive phase with cold eastern Pacific temperatures. These SST conditions have been shown to be associated with an increase in precipitation over most of the Amazon except the south and south-west. If ongoing changes continue we expect these to be generally beneficial for forests in those regions where there is an increase in precipitation with the exception of floodplain forests. An increase in flood-pulse height and duration could lead to increased mortality at higher levels of the floodplain and, over the long term, to a lateral shift of the zonally stratified floodplain forest communities. Negative effects on forests are mainly expected in the south-west and south, which have become slightly drier and hotter, consistent with tree mortality trends observed at the RAINFOR forest plot census network.
The vulnerability of Amazonian rainforest, and the ecological services it provides, depends on an adequate supply of dry-season water, either as precipitation or stored soil moisture. How the rain-bearing South American monsoon will evolve across the twenty-first century is thus a question of major interest. Extensive savanization, with its loss of forest carbon stock and uptake capacity, is an extreme although very uncertain scenario. We show that the contrasting rainfall projections simulated for Amazonia by 36 global climate models (GCMs) can be reproduced with empirical precipitation models, calibrated with historical GCM data as functions of the large-scale circulation. A set of these simple models was therefore calibrated with observations and used to constrain the GCM simulations. In agreement with the current hydrologic trends7, 8, the resulting projection towards the end of the twenty-first century is for a strengthening of the monsoon seasonal cycle, and a dry-season lengthening in southern Amazonia. With this approach, the increase in the area subjected to lengthy—savannah-prone—dry seasons is substantially larger than the GCM-simulated one. Our results confirm the dominant picture shown by the state-of-the-art GCMs, but suggest that the ‘model democracy’ view of these impacts can be significantly underestimated.
Atmospheric carbon dioxide records indicate that the land surface has acted as a strong global carbon sink over recent decades1, 2, with a substantial fraction of this sink probably located in the tropics3, particularly in the Amazon4. Nevertheless, it is unclear how the terrestrial carbon sink will evolve as climate and atmospheric composition continue to change. Here we analyse the historical evolution of the biomass dynamics of the Amazon rainforest over three decades using a distributed network of 321 plots. While this analysis confirms that Amazon forests have acted as a long-term net biomass sink, we find a long-term decreasing trend of carbon accumulation. Rates of net increase in above-ground biomass declined by one-third during the past decade compared to the 1990s. This is a consequence of growth rate increases levelling off recently, while biomass mortality persistently increased throughout, leading to a shortening of carbon residence times. Potential drivers for the mortality increase include greater climate variability, and feedbacks of faster growth on mortality, resulting in shortened tree longevity5. The observed decline of the Amazon sink diverges markedly from the recent increase in terrestrial carbon uptake at the global scale1, 2, and is contrary to expectations based on models6.
Originally conceived to conserve iconic landscapes and wildlife, protected areas are now expected to achieve an increasingly diverse set of conservation, social and economic objectives. The amount of land and sea designated as formally protected has markedly increased over the past century, but there is still a major shortfall in political commitments to enhance the coverage and effectiveness of protected areas. Financial support for protected areas is dwarfed by the benefits that they provide, but these returns depend on effective management. A step change involving increased recognition, funding, planning and enforcement is urgently needed if protected areas are going to fulfil their potential.
In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?
Since his inauguration on January 1, 2019, Jair Bolsonaro, a declared right-wing candidate nicknamed “Tropical Trump,” has introduced measures to reduce environmental restrictions on livestock farming, the main greenhouse gas (GHG) producing sector in Brazil that is responsible for most of the deforestation in the country. This dangerous relationship between politics and livestock farming in Brazil is detrimental to environmental conservation. Politicians are introducing measures that facilitate the expansion of this type of farming, which in turn provides inputs for the food industry, i.e. agribusiness, which in turn finances politics, thus producing a dangerous cycle in forest conservation.
The Brazilian Amazon harbours 70 % of the world's tropical forests and is essential to the country's economy because it maintains biodiversity, sustains the livelihoods of the indigenous people and local communities, and provides ecosystem services such as water production, soil stabilization, flood prevention, and climate regulation. In the last three decades, the Brazilian government has established a regional protected area (PA) network that currently covers approximately 48 % of the region. Despite their importance, some sectors of the Brazilian society have argued that the expansion of the PAs across the region hampers the local economic development, because they make less area available for non-forest economic activities such as large-scale agriculture, mining, and power generation. In this study, we analysed the relationship between local economic growth and PA coverage in 516 municipalities in the Brazilian Amazon from 2004 to 2014. We modelled the impact of the coverage of the three types of PAs (strictly-protected, multiple-use, and indigenous lands) on the (i) compound annual growth rate (CAGR) of the real gross domestic product per capita (GDP per capita), and (ii) real gross value added per capita (GVA per capita) of the agriculture, industry, services, and government sectors in each municipality. The models also considered the following control variables at the municipal level: area, age, per capita GPD in 2004 (or per capita GVAs in 2004), population growth rate between 2004 and 2014, education index, deforested area outside PA per capita, deforested area inside PA per capita, degraded area outside PA per capita, degraded area inside PA per capita, and presence of illegal mining within PA. We applied spatial Durbin error models (SDEM) to analyse the direct, indirect, and total impacts of the PAs on the local economic growth. We did not find a statistically significant relationship between the local economic growth and PA coverage in any of the three PA groups evaluated. Only the total impact of the GVA per capita of the industry was negatively correlated with the coverage of the strictly-protected PAs. Our findings do not support the arguments used by some interest groups of the Brazilian society that the social and environmental gains generated through the expansion of PAs across the region constrain the overall local economic growth.
The inauguration of Jair Bolsonaro as Brazil’s new president has heralded a rapid acceleration of the erosion of environmental protection measures in the country. Brazil’s scientific community should rally to provide evidence that this is economically and socially unwise.
The Amazonian forest’s ability to provide environmental services is threatened by anthropogenic forcing at various scales, such as deforestation, fire, global and regional climate change, and extreme events. In addition to the impacts resulting from each one of these drivers, the synergistic effects potentially increase the risks. In the light of the above, this chapter aims to evaluate the future prospects for the Amazon in a scenario of 4 °C or higher warming resulting from anthropogenic climate change and the related hydrological cycle changes. Future climate scenarios project progressively higher warming that may exceed 4 °C in Amazonia in the second half of the century, particularly during the dry season in the region. Associated with these scenarios, it is projected a reduction of precipitation year-round, being a substantial reduction predominantly in the dry and transition seasons and smaller reductions of the order of 5% for the SH summer. Evaluating the consequences of such substantial climatic change, several negative effects in Amazonia can be anticipated, including short-term hydrological changes similar to the events associated to the extreme 2005 and 2010 droughts, and longer time-scale modifications of broad scale characteristics such as different biome distribution. Based on hydrological models, it is generally expected a reduction in river discharges associated to precipitation decreases and temperature increases brought about by projected climate change, but with the magnitude of the changes differing between models. The future climate change scenarios imply important changes in biomes distribution over Amazonia, with potential expansion of savannah and caatinga over large areas currently occupied by tropical forests. It is necessary a reduction to nearly zero in tropical deforestation and reducing land-cover emissions and mitigating climate change to avoid a dangerous interference with the ability of natural ecosystems to adapt to these possible changes.
The tropics contain the overwhelming majority of Earth's biodiversity: their terrestrial, freshwater and marine ecosystems hold more than three-quarters of all species, including almost all shallow-water corals and over 90% of terrestrial birds. However, tropical ecosystems are also subject to pervasive and interacting stressors, such as deforestation, overfishing and climate change, and they are set within a socio-economic context that includes growing pressure from an increasingly globalized world, larger and more affluent tropical populations, and weak governance and response capacities. Concerted local, national and international actions are urgently required to prevent a collapse of tropical biodiversity.
Normalized Difference Vegetation Index (NDVI) is an important remote measurement in agriculture because it has a high correlation with crop growth and yield result. In this paper, we present a methodology to predict the NDVI by training a crop growth model with historical data. Although we use a very simple soybean growth model, the methodology could be extended to other crops and more complex models. The training process is an optimization problem, that is solved using the spectral projected gradient method. The quality of the prediction is measured by computing the Root-Mean-Square Error (RMSE) between predicted and true values, obtaining an error lower than 9%, which improves the results obtained by simple forecast techniques used as baseline estimators.
Tropical forests are an undervalued asset in meeting the greatest global challenges of our time-averting climate change and promoting development. Despite their importance, tropical forests and their ecosystems are being destroyed at a high and increasing rate in most forest-rich countries. The good news is that science, economics, and politics are aligned to support a major international effort over the next five years to reverse tropical deforestation. Why Forests? Why Now? synthesizes the latest evidence on the importance of tropical forests in a way that is accessible to anyone interested in climate change and development and to readers already familiar with the problem of deforestation. It makes the case to decision makers in rich countries that rewarding developing countries for protecting their forests is urgent, affordable, and achievable.
This book is open access under a CC BY 4.0 license.
This volume presents an Empirical Model of Global Climate developed by the authors and uses that model to show that global warming will likely remain below 2ºC, relative to preindustrial, throughout this century provided: a) both the unconditional and conditional Paris INDC commitments are followed; b) the emission reductions needed to achieve the Paris INDCs are carried forward to 2060 and beyond.
The first section of the book provides a short overview of Earth’s climate system, describing and contrasting climatic changes throughout the planet’s history and anthropogenic changes post-Industrial Revolution. The second section describes the climate model developed by the authors (Canty et al., Atmospheric Chemistry and Physics, 2013) and contrasts the model with climate models used in the Intergovernmental Panel on Climate Change (IPCC) 2013 Report. Chapter 3 examines both the unconditional (i.e., firm commitments) and conditional Paris INDCs (commitments contingent on financial flow and/or technology transfer) through the lens of their climate model and concludes that if all of the Paris INDCs are followed, then they are indeed a beacon of hope for Earth’s climate. The fourth part of the book offers a perspective of energy needs and subsequent emissions reductions required to meet the Paris temperature goals, illuminating challenges faced both in the developing world and the developed world.
Throughout the book, easy-to-understand charts and graphics illustrate concepts. The scientific basis of Chapters 2 and 3 was first presented in a keynote session of the 96th Annual Meeting of the American Meteorological Society in January, 2016.
Climate change is increasingly a part of the human experience. As the problem worsens, the cooperative dilemma that the issue carries has become evident: climate change is a complex problem that systematically gets insufficient answers from the international system.
This book offers an assessment of Brazil’s role in the global political economy of climate change. The authors, Eduardo Viola and Matías Franchini expertly review and answer the most common and widely cited questions on whether and in which way Brazil is aggravating or mitigating the climate crisis, including: Is it the benign, cooperative, environmental power that the Brazilian government claims it is? Why was it possible to dramatically reduce deforestation in the Amazon (2005-2010) and, more recently, was there a partial reversion?
The book provides an accessible―and much needed―introduction to all those studying the challenges of the international system in the Anthropocene. Through a thorough analysis of Brazil in perspective vis a vis other emerging countries, this book provides an engaging introduction and up to date assessment of the climate reality of Brazil and a framework to analyze the climate performance of major economies, both on emission trajectory and policy profile: the climate commitment approach. Brazil and Climate Change is essential reading for all students of Environmental Studies, Latin American Studies, International Relations and Comparative Politics.
The recently published Intergovernmental Panel on Climate Change (IPCC) projections to 2100 give likely ranges of global temperature increase in four scenarios for population, economic growth and carbon use. However, these projections are not based on a fully statistical approach. Here we use a country-specific version of Kaya's identity to develop a statistically based probabilistic forecast of CO 2 emissions and temperature change to 2100. Using data for 1960-2010, including the UN's probabilistic population projections for all countries, we develop a joint Bayesian hierarchical model for Gross Domestic Product (GDP) per capita and carbon intensity. We find that the 90% interval for cumulative CO 2 emissions includes the IPCC's two middle scenarios but not the extreme ones. The likely range of global temperature increase is 2.0-4.9 °C, with median 3.2 °C and a 5% (1%) chance that it will be less than 2 °C (1.5 °C). Population growth is not a major contributing factor. Our model is not a business as usual' scenario, but rather is based on data which already show the effect of emission mitigation policies. Achieving the goal of less than 1.5 °C warming will require carbon intensity to decline much faster than in the recent past.
Recently developed methodologies such as climate reanalysis make it possible to create a historical record of climate systems. This paper proposes a methodology called Hydrological Retrospective (HR), which essentially simulates large rainfall datasets, using this as input into hydrological models to develop a record of past hydrology, making it possible to analyze past floods and droughts. We developed a methodology for the Amazon basin, where studies have shown an increase in the intensity and frequency of hydrological extreme events in recent decades. We used eight large precipitation datasets (more than 30 years) as input for a large scale hydrological and hydrodynamic model (MGB-IPH). HR products were then validated against several in situ discharge gauges controlling the main Amazon sub-basins, focusing on maximum and minimum events. For the most accurate HR, based on performance metrics, we performed a forecast skill of HR to detect floods and droughts, comparing the results with in-situ observations. A statistical temporal series trend was performed for intensity of seasonal floods and droughts in the entire Amazon basin. Results indicate that HR could represent most past extreme events well, compared with in-situ observed data, and was consistent with many events reported in literature. Because of their flow duration, some minor regional events were not reported in literature but were captured by HR. To represent past regional hydrology and seasonal hydrological extreme events, we believe it is feasible to use some large precipitation datasets such as i) climate reanalysis, which is mainly based on a land surface component, and ii) datasets based on merged products. A significant upward trend in intensity was seen in maximum annual discharge (related to floods) in western and northwestern regions and for minimum annual discharge (related to droughts) in south and central-south regions of the Amazon basin. Because of the global coverage of rainfall datasets, this methodology can be transferred to other regions for better estimation of future hydrological behavior and its impact on society.
Brazil confirmed targets for reducing greenhouse gas emissions in 2008, including an 80% reduction in deforestation in the Amazon by 2020. With this in mind, we investigated the trade-off between environmental conservation and economic growth in the Amazon. The aim of this study is to project the economic losses and land-use changes resulting from a policy to control deforestation and the rise in land productivity that is necessary to offset those losses. We developed a Dynamic Interregional Computable General Equilibrium Model for 30 Amazon regions with a land module allowing conversion between types of land. The results have shown that the most affected regions would be soybeans and cattle producers as well as regions dominated by family farms. To offset these impacts, it was estimated that an annual gain of land productivity of approximately 1.4% would be required.
Developing and institutionalizing cross-sectoral approaches to sustainable land use remains a crucial, yet politically contested, objective in global sustainability governance. There is a widely acknowledged need for more integrated approaches to sustainable land use that reconcile multiple landscape functions, sectors and stakeholders. However, this faces a number of challenges in practice, including the lack of policy coherence and institutional conflicts across agricultural and forest sectors. In this context, the global climate change mitigation mechanism of “reducing emissions from deforestation and forest degradation” (REDD+) has been flagged as a unique opportunity to stimulate the development and institutionalization of more integrated, “landscape” approaches to sustainable land use. In this article, we provide a reality check for the prospects of REDD+ to deliver on this promise, through analyzing three pioneer cases of REDD+ development and implementation in Brazil, Ecuador, and Mexico. We analyze how REDD+ has operated in each of these three contexts, based on field work, key-informant interviews, and analysis of primary and secondary documents. Our findings suggest that REDD+ has stimulated development of “niche” sustainable land-use investments in each case, which aim to integrate forest conservation and agricultural development goals, but has done so while competing with business-as-usual incentives. We conclude that national and international political commitment to more integrated and sustainable land-use approaches is a precondition for, rather than a result of, transformative REDD+ interventions.
The Paris climate agreement aims at holding global warming to well below 2 degrees Celsius and to “pursue efforts” to limit it to 1.5 degrees Celsius. To accomplish this, countries have submitted Intended Nationally Determined Contributions (INDCs) outlining their post-2020 climate action. Here we assess the effect of current INDCs on reducing aggregate greenhouse gas emissions, its implications for achieving the temperature objective of the Paris climate agreement, and potential options for overachievement. The INDCs collectively lower greenhouse gas emissions compared to where current policies stand, but still imply a median warming of 2.6–3.1 degrees Celsius by 2100. More can be achieved, because the agreement stipulates that targets for reducing greenhouse gas emissions are strengthened over time, both in ambition and scope. Substantial enhancement or over-delivery on current INDCs by additional national, sub-national and non-state actions is required to maintain a reasonable chance of meetin
The Paris Agreement duly reflects the latest scientific understanding of systemic global warming risks. Limiting the anthropogenic temperature anomaly to 1.5–2 °C is possible, yet requires transformational change across the board of modernity.
Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.
This paper investigates the contribution of agricultural output prices and policies to the reduction in Amazon deforestation in the 2000s. Based on a panel of Amazon municipalities from 2002 through 2009, we first show that deforestation responded to agricultural output prices. After controlling for price effects, we find that conservation policies implemented beginning in 2004 and 2008 significantly contributed to the curbing of deforestation. Counterfactual simulations suggest that conservation policies avoided approximately 73,000 km2 of deforestation, or 56 per cent of total forest clearings that would have occurred from 2005 through 2009 had the policies adopted beginning in 2004 and 2008 not been introduced. This is equivalent to an avoided loss of 2.7 billion tonnes of stored carbon dioxide.