Climate Change Use Research Articles

An improved deep learning procedure for statistical downscaling of climate data

Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is known about their suitability for extrapolating future CC scenarios. Here we analyzed this issue by downscaling maximum, and minimum temperature over the Egyptian domain based on one General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP4.5 and SSP8.5 from CMIP6 using Convolutional Neural Network (CNN) herein after called CNNSD. The downscaled maximum and minimum temperatures based CNNSD was able to reproduce the observed climate over historical and future periods at a finer resolution (0.1°), reducing the biases exhibited by the original scenario. To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. The downscaled analysis showed that maximum and minimum temperatures are expected to rise by 4.8 °C and 4.0 °C, respectively, in the future (2015–2100), compared to the historical period, under the moderate scenario (SSP4.5). Meanwhile, under the Fossil-fueled Development scenario (SSP8.5), these values will rise by 6.3 °C and 4.2 °C, respectively as analyzed by the CNNSD. The developed approach could be used not only in Egypt but also in other developing countries, which are especially vulnerable to climate change and has a scarcity of related research. The established downscaled approach's supply can be used to provide climate services, as a driver for impact studies and adaptation decisions, and as information for policy development. More research is needed, however, to include multi-GCMs to quantify the uncertainties between GCMs and SSPs, improving the outputs for use in climate change impacts and adaptations for food and nutrition security.

Climate Change and Fuel Wood Energy Use in Cold Desert of Drass in the Himalaya, India

This paper has identified the impacts of climate change on fuel wood energy use in Drass, District Kargil, Ladakh in the Himalayan region and drawn the policy implication to improve the livelihoods of the people in the context of climate change using a case study approach. The study reveals that the major source of energy used and consumed in Drass is fuel wood. Almost all energy requirements for cooking and heating are met by burning fuel wood which produces smoke, cause air pollution, emits carbon dioxide and thereby contributes to climate change. Less than half of the farmers grow fuel wood in gardens or homesteads. Most farmers purchased fuel wood from the market. Awareness of environmental effects of using fuel wood is significant. Despite this, the exposure of women to indoor air pollution from biomass fuel combustion was significantly high. The use of cleaner energy was significantly low due to economic reasons as well as inadequate accessibility and availability. Forest resources are meager in Drass. Therefore, efforts should be made to increase forest cover with the stronger participation of local communities. The government sponsored schemes targeting efficient energy usage and energy insecurity should be implemented in letter and spirit through greater people's participation.

India and Climate Change: Old Traditions, New Strategies

Climate change emerged as a serious diplomatic concern for India in the early 1990s. India has since been a staunch champion of the developing world, in multilateral climate negotiations; from playing a key role in establishing the principle of common but differentiated responsibility and respective capabilities, to forging alliances with countries with shared developmental concerns. Faced with the growing impact of climate change and changing geopolitics, India’s foreign policy on climate change has undergone significant shifts during two decades of multilateral climate talks. First, India’s willingness to undertake domestic climate action as a result of its changing economic status and, second, the role of the political leadership—since Prime Minister Narendra Modi came to power in 2014—in shaping the climate discourse both in the country and abroad. India has, in recent years, shed its defensive, naysayer strategy at climate negotiations and led several multilateral initiatives for climate action. As India celebrates 75 years of its Independence, it is forging a new, confident leadership on issues of climate change. While there is continuity in India’s traditional stance on finance and technology transfer during negotiations, Indian foreign policy has begun to display a marked change in the strategic use of climate change to assert greater power on the global stage.

What Justice? Whose justice?: Rethinking climate justice through climate change impacts and options for adaptation in Africa

Climate change is considered a justice issue based on the causes of the phenomenon, its impacts and polices designed to manage it. Previous studies have revealed that Africa and the entire developing world contribute less to Green House Gas emissions (GHG) which is the leading cause of climate change, but they suffer the most from its consequences, whereas the first world, whose contribution to GHG is immense but the impact of the phenomenon to them is minimal. In this article, I made use of climate change impacts and adaptation options to scrutinise the concept of climate justice in Africa. The article sought to establish whether or not climate justice has a place on the African continent. To achieve that objective, I critically assessed climate change impacts and options for adaptation in various African countries vis-à-vis climate justice. The study found that climate justice is a farce for Africa because climate disruptions affect nations differently and adaptive capacities differ as well. It also emerged that climate change-based impacts in Africa largely affect women and poor people. Lastly, the desire for profit among developed countries and climate-based organisations deters the urgency for climate justice across the globe.

Stocks of Carbon in Logs and Timber Products from Forest Management in the Southwestern Amazon

Amazon forest management plans have a variety of effects on carbon emissions, both positive and negative. All of these effects need to be quantified to assess the role of this land use in climate change. Here, we contribute to this effort by evaluating the carbon stocks in logs and timber products from an area under forest management in the southeastern portion of Acre State, Brazil. One hundred and thirty-six trees of 12 species had DBH ranging from 50.9 cm to 149.9 cm. Basic wood density ranged from 0.3 cm−3 to 0.8 g cm−3 with an average of 0.6 g cm−3. The logs had a total volume of 925.2 m3, biomass of 564 Mg, and carbon stock of 484.2 MgC. The average volumetric yield coefficient (VYC) was 52.3% and the carbon yield coefficient (CYC) was 53.2% for logs of the 12 species. The sawn-wood products had a total volume of 484.2 m3, biomass of 302.6 Mg, and carbon stock of 149.9 MgC. Contributions of the different species to the total carbon stored in sawn-wood products ranged from 2.2% to 21.0%. Means and standard deviations for carbon transferred to sawn-wood products per-species from the 1252.8-ha harvested area ranged from 0.4 ± 1.1 MgC to 2.9 ± 0.4 MgC, with the largest percentages of the total carbon stored in wood products being from Dipteryx odorata (21.0%), Apuleia leiocarpa (18.7%), and Eschweilera grandiflora (11.7%). A total of 44,783 pieces of sawn lumber (such as rafters, planks, boards, battens, beams, and small beams) was obtained from logs derived from these trees. Lumber production was highest for boards (54.6% of volume, 47.4% of carbon) and lowest for small beams (1.9% of volume, 2.3% of carbon). The conversion factor for transforming log volume into carbon stored in sawn-wood products was 16.2%. Our results also show that species that retain low amounts of carbon should be allowed to remain in the forest, thereby avoiding low sawmill yield (and consequent generation of waste) and allowing these trees to continue fulfilling environmental functions.

Evaluating Water Balance Variables under Land Use and Climate Projections in the Upper Choctawhatchee River Watershed, in Southeast US

Changes in water balance variables are essential in planning and management. Two major factors affecting these variables are climate change and land use change. Few researches have been done to investigate the combined effect of the land use change and climate change using projections. In this study the hydrological processes in Upper Choctawhatchee River Watershed were modeled using the Soil and Water Assessment Tool (SWAT) to investigate the impacts of climate and land use change. We integrated land use projection based in the Shared Socioeconomic Pathways with future climate data to study the combined effects on Hydrological response of the watershed. Future rainfall and temperature, for two time periods, were obtained using General Climate Models to provide SWAT with the climatic forcing in order to project water balance variables. The simulation was carried out under two radiative forcing pathways of RCP4.5 and RCP6.0. Land use change focused on urbanization dominated the climate changes. Impacts on water balance variables differed seasonally. Results showed surface runoff experienced major changes under both emissions scenarios in some months up to 5 times increase. Among the water balance variables, evapotranspiration (ET) as the least dominant pathway for water loss showed the modest changes with the largest decrease during fall and summer. Projection indicated more frequent extreme behavior regarding water balance during midcentury. Discharge was estimated to increase through the year and the highest changes were projected during summer and fall with 186.3% increase in November under RCP6.0. Relying on rainfall for farming along with reduced agricultural landuse (11.8%) and increased urban area (47%) and population growth would likely make the water use efficiency critical. The model demonstrated satisfactory performance, capturing the hydrologic parameters. It thus can be used for further modelling of water quality to determine the sustainable conservation practices and extreme weather events such as hurricane and tropical storms.

‘Climate Change as a Spice’: Brokering Environmental Knowledge in Bangladesh’s Development Industry

ABSTRACT This article examines whether the use of climate change as a ‘spice’ in order to attract donor funding may instead exacerbate existing environmental problems. The World Bank’s latest adaptation project in coastal Bangladesh aims to create higher and wider embankments against rising sea levels. This disregards a long history of how embankments, by stopping beneficial monsoon inundations, result in dying rivers and damaging floods that devastate rural livelihoods. Bangladeshi ‘development brokers’ must therefore balance their roles as project employees supporting embankments as adaptation, and as locals knowledgeable about their harmful effects. The article shows how donors, NGOs, consultants and government bodies with different agendas, priorities and knowledge backgrounds ‘translate’ climate change to legitimise their activities. It contributes to debates about the politics of environmental knowledge production by arguing that development brokerage helps explain why some climate adaptation projects increase environmental vulnerability, while others address local needs.

The need for bottom-up assessments of climate risks and adaptation in climate-sensitive regions

Studies of climate change at specific intervals of future warming have primarily been addressed through top-down approaches using climate projections and modelled impacts. In contrast, bottom-up approaches focus on the recent past and present vulnerability. Here, we examine climate signals at different increments of warming and consider the need to reconcile top-down and bottom-up approaches. We synthesise insights from recent studies in three climate-sensitive systems where change is a defining feature of the human-environment system. Whilst top-down and bottom-up approaches generate complementary insights into who and what is at risk, integrating their results is a much-needed step towards developing relevant information to address the needs of immediate adaptation decisions. Top-down studies of climate change use climate projections and modelled impacts, whereas bottom-up assessments focus on the recent past and present vulnerability. This Perspective argues that these approaches must be integrated to address the needs of immediate adaptation decisions.

Social Media, Science, and Attack Discourse: How Twitter Discussions of Climate Change Use Sarcasm and Incivility

Conflict in online discussions of science has the potential to polarize individuals’ perceptions of science, yet science communication scholarship has paid little attention to systematic study of how verbal attacks play out in online discussions of science. This study analyzes sarcasm and incivility in Twitter discussions of climate change during an extreme weather event ( n = 4,094). We found instances of incivility and sarcasm were low overall. Incivility was used in association with political topics, and both incivility and sarcasm were used alongside skeptical perspectives of climate change and by those who mention right-leaning politics in their profiles.

Weaponizing nature: The geopolitical ecology of the US Navy’s biofuel program

The United States military is treating climate change as a crucial factor in its preparation for future conflicts. This concern manifests not only in strategic planning and forward-looking documents, but also in building infrastructural capacity and material provision. Yet, the impetus to ‘green’ the military goes beyond the deployment of existing technologies. We examine several facets of the military's role as an environmental actor, particularly through its promotion of the US Navy's ‘Great Green Fleet’ (GGF), which actively supports the development of advanced biofuels by subsidizing their development and facilitating wider marketization. The GGF promises to reduce military reliance on conventional fossil fuels and reconfigure its energy sourcing, thus reducing dependence on imported hydrocarbons; this is with an eye towards ultimately severing the logistical relationship between existing energy infrastructures and the spaces of military intervention. Taking an integrated lens of political ecology and geopolitics - ‘geopolitical ecology’ - we seek to provide an understanding of the production of weaponized nature. We demonstrate that the US military's discursive use of climate change to justify the provision of new military hardware and advanced biofuels promotes a vision of resource conflicts to support the development of technologies to overcome the constraints to delivery of fuel to emergent front lines. We argue that while this may appear to be militarized greenwashing, it signals a shift in the logics and practices of fuel sourcing driven by a dystopian vision of climate change, which the US military played a significant role in creating.

Fugitive Methane and the Role of Atmospheric Half-Life

Because methane has an initial GWP (global warming potential) 120x that of carbon dioxide, concern about leaked “fugitive” methane often dominates discussions of natural gas policy. But this high GWP can easily be misinterpreted to conclude that natural gas is a much greater greenhouse danger when used for electric power generation vs. coal. In determining policy, particularly with regard to legacy issues (global warming for future generations) it is essential to take full account of the short 8.6-year lifetime of methane in the atmosphere. Moreover, the Intergovernmental Panel on Climate Change use of time “horizon” must be understood properly; it refers to an average from the present, not a time that lies in the future. The IPCC GWP value cannot be used directly to estimate the effect unless careful account is taken of the lighter weight of the methane molecule compared to that of carbon dioxide. We discusses severalways to take these issues into account, and show that for a wide range of leakages and legacy goals, fugitive methane is typically not a critical issue, and that even with a few percent leakage, natural gas is preferable to coal for legacy global warming considerations.

Teleconnection responses in multi-GCM driven CORDEX RCMs over Eastern Africa

The ability of climate models to simulate atmospheric teleconnections provides an important basis for the use and analysis of climate change projections. This study examines the ability of COordinated Regional climate Downscaling EXperiment models, with lateral and surface boundary conditions derived from Coupled Global Climate Models (CGCMs), to simulate the teleconnections between tropical sea surface temperatures and rainfall over Eastern Africa. The ability of the models to simulate the associated changes in atmospheric circulation patterns over the region is also assessed. The models used in the study are Rossby Centre regional atmospheric model (RCA) driven by eight CGCMs and COnsortium for Small scale MOdeling (COSMO) Climate Limited-area Modelling (COSMO-CLM or CCLM) driven by four of the same CGCMs. Teleconnection patterns are examined using correlation, regression and composite analysis. In order to identify the source of the errors, CGCM-driven regional climate model (RCM) results are compared with ERA-Interim driven RCM results. Results from the driving CGCMs are also analyzed. The RCMs driven by reanalysis (quasi-perfect boundary conditions) successfully capture rainfall teleconnections in most examined regions and seasons. Our analysis indicates that most of the errors in simulating the teleconnection patterns come from the driving CGCMs. RCMs driven by MPI-ESM-LR, HadGEM2-ES and GFDL-ESM2M tend to perform relatively better than RCMs driven by other CGCMs. CanESM2 and MIROC5, and their corresponding downscaled results capture the teleconnections in most of the sub-regions and seasons poorly. This highlights the relative importance of CGCM-derived boundary conditions in the downscaled product and the need to improve these as well as the RCMs themselves. Overall, the results produced here will be very useful in identifying and selecting CGCMs and RCMs for the use of climate change projecting over the Eastern Africa.

How certain is 'certain'? Exploring how the English-language media reported the use of calibrated language in the Intergovernmental Panel on Climate Change's Fifth Assessment Report.

This article presents findings from an analysis of English-language media reports following the publication of the fifth Intergovernmental Panel on Climate Change Assessment Report in September 2013. Focusing on the way they reported the Intergovernmental Panel on Climate Change's use of 'calibrated' language, we find that of 1906 articles relating to the issuing of the report only 272 articles (14.27%) convey the use of a deliberate and systematic verbal scale. The Intergovernmental Panel on Climate Change's carefully calibrated language was rarely discussed or explicated, but in some instances scientists, political actors or journalists would attempt to contextualise or elaborate on the reported findings by using analogies to other scientific principles or examples of taking action despite uncertainty. We consider those analogies in terms of their efficacy in communicating (un)certainty.

The Role of Civil Society in Recalibrating Conservation Science Incentives

The Role of Civil Society in Recalibrating Conservation Science Incentives

Validation of a stochastic temperature generator focusing on extremes, and an example of use for climate change

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 59:61-75 (2014) - DOI: https://doi.org/10.3354/cr01201 Validation of a stochastic temperature generator focusing on extremes, and an example of use for climate change S. Parey1,*, T. T. H. Hoang1, D. Dacunha-Castelle2 1EDF/R&D, 6 quai Watier, 78 401 Chatou Cedex, France 2Laboratoire de Mathématiques, Université Paris 11, Orsay, France *Corresponding author: sylvie.parey@edf.fr ABSTRACT: We present a stochastic seasonal functional heteroscedastic auto-regressive model developed to simulate daily (minimum, maximum, or mean) temperature time series coherent with observed time series and designed to reliably reproduce extreme values through a careful study of extremes and the fact that the tails of the distribution are bounded. The model was first validated using different daily minimum and maximum weather station time series over Eurasia and the US in different climatic regions. The model was able to produce coherent results both for the bulk of the distribution and for its extremes and was able to produce higher or lower extreme values than observed. A possible use in the climate change context was then tested. We fit the model over the first part of a long temperature time series and then used it to simulate a large number of possible trajectories for the second part when temperature increased. Two approaches were tested to do so, one based on a simple mean change in mean and variance and the other in considering the full seasonalities and trends estimated over the observed second part of the time series. Both approaches yielded good results, both for the bulk and for the extremes of the temperature distribution over the second part of the period. However, the second approach allowed us to take interannual variability changes into account, which leads to more realistic results when this occurs. Our results support the use of this tool for statistical downscaling, enabling the reliable reproduction of temperature extremes. KEY WORDS: Daily temperature · Stochastic modeling · Extremes Full text in pdf format PreviousNextCite this article as: Parey S, Hoang TTH, Dacunha-Castelle D (2014) Validation of a stochastic temperature generator focusing on extremes, and an example of use for climate change. Clim Res 59:61-75. https://doi.org/10.3354/cr01201 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 59, No. 1. Online publication date: February 04, 2014 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2014 Inter-Research.

Observed and simulated estimates of the meridional overturning circulation at 26.5° N in the Atlantic

Abstract. Daily timeseries of the meridional overturning circulation (MOC) estimated from the UK/US RAPID/MOCHA array at 26.5° N in the Atlantic are used to evaluate the MOC as simulated in two global circulation models: (I) an 8-member ensemble of the coupled climate model ECHAM5/MPI-OM, and (II) the ECCO-GODAE state estimate. In ECHAM5/MPI-OM, we find that the observed and simulated MOC have a similar variability and time-mean within the 99% confidence interval. In ECCO-GODAE, we find that the observed and simulated MOC show a significant correlation within the 99% confidence interval. To investigate the contribution of the different transport components, the MOC is decomposed into Florida Current, Ekman and mid-ocean transports. In both models, the mid-ocean transport is closely approximated by the residual of the MOC minus Florida Current and Ekman transports. As the models conserve volume by definition, future comparisons of the RAPID/MOCHA mid-ocean transport should be done against the residual transport in the models. The similarity in the variance and the correlation between the RAPID/MOCHA, and respectively ECHAM5/MPI-OM and ECCO-GODAE MOC estimates at 26.5° N is encouraging in the context of estimating (natural) variability in climate simulations and its use in climate change signal-to-noise detection analyses. Enhanced confidence in simulated hydrographic and transport variability will require longer observational time series.

Environmental Nonprofit Campaigns and State Competition: Influences on Climate Policy in California

Over the past 20 years, the US Federal Government has been considered to be intransigent in its response to climate change by many commentators and not-for-profit environmental advocacy organizations (nonprofits). An enduring source of pressure on the US Government has been nonprofit campaigns operating at both a state and federal level. Six US environmental nonprofits representing a diversity of resources and prominence were selected for an in-depth examination of their climate-focused campaigns. Given the resistance at the federal level, these nonprofits have undertaken state-focused campaigns to achieve adequate climate policy development. This research examined some climate campaigns in California by the selected nonprofits that have supported, enhanced, and influenced the Californian Government's efforts to address climate change. The campaigns have gained leverage from existing state competition for economic advancement and political leadership on issues of public concern. In addition, they appear to have benefited from a high level of environmental awareness in the community, a history of progressive environmental legislation, Governor Schwarzenegger's use of climate change to differentiate his political leadership, and strong public trust of nonprofits. Recent climate-related political pledges and legislative changes at a federal level are convergent with the nonprofit-influenced, state-level developments.

Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps

An evaluation is undertaken of the statistics of daily precipitation as simulated by five regional climate models using comprehensive observations in the region of the European Alps. Four limited area models and one variable‐resolution global model are considered, all with a grid spacing of 50 km. The 15‐year integrations were forced from reanalyses and observed sea surface temperature and sea ice (global model from sea surface only). The observational reference is based on 6400 rain gauge records (10–50 stations per grid box). Evaluation statistics encompass mean precipitation, wet‐day frequency, precipitation intensity, and quantiles of the frequency distribution. For mean precipitation, the models reproduce the characteristics of the annual cycle and the spatial distribution. The domain mean bias varies between −23% and +3% in winter and between −27% and −5% in summer. Larger errors are found for other statistics. In summer, all models underestimate precipitation intensity (by 16–42%) and there is a too low frequency of heavy events. This bias reflects too dry summer mean conditions in three of the models, while it is partly compensated by too many low‐intensity events in the other two models. Similar intermodel differences are found for other European subregions. Interestingly, the model errors are very similar between the two models with the same dynamical core (but different parameterizations) and they differ considerably between the two models with similar parameterizations (but different dynamics). Despite considerable biases, the models reproduce prominent mesoscale features of heavy precipitation, which is a promising result for their use in climate change downscaling over complex topography.

Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 11:137-148 (1999) - doi:10.3354/cr011137 Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain Mikhail A. Semenov*, Roger J. Brooks** IACR-Long Ashton Research Station, Department of Agricultural Sciences, University of Bristol, Long Ashton, Bristol BS41 9AF, United Kingdom *E-mail: mikhail.semenov@bbsrc.ac.uk **Present address: Department of Management Science, The Management School, University of Lancaster, Lancaster LA1 4YX, United Kingdom ABSTRACT: The paper describes a method for the spatial interpolation of the site-specific LARS-WG stochastic weather generator to produce 'realistic' daily weather data for the gaps between observed sites. One of the uses of LARS-WG has been site-scale climate change impact assessments. However, such assessments are often applied across regions and so there is a need for an interpolation method to provide input daily weather at many sites or grid-boxes where observed weather data is not available. The interpolation method devised combines the local interpolation of the weather generator parameters from observed sites near the unobserved location with the use of globally interpolated monthly mean statistics for a large number of sites. Thin plate smoothing splines with elevation as an independent variable were used for the global interpolation of mean monthly rainfall and temperature. The data sets used allow daily weather to be generated for any location in Great Britain and the methodology was tested at 3 locations with different local characteristics. The interpolation method showed a good performance at the 3 sites when compared to the observed data, the main differences occurring when the spline method was unable to reproduce closely the observed mean values. The limitations of the interpolation method, its applicability to other regions and its potential use in climate change and other studies are also discussed. KEY WORDS: Weather generator · Spatial interpolation · Climate modelling Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 11, No. 2. Online publication date: March 22, 1999 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 1999 Inter-Research.

Integrating Physical and Social Modelling: The Example of Climate Change

Existing integrated assessment models for climate change use conventional economic approaches to implement the social representations. This paper argues that these economic approaches would be inappropriate if economists were rational in the sense they assume their agents to be. A straightforward stochastic, cost-benefit analysis shows that conditions of application of any model should be identified and investigated unless (a) it is extremely expensive to determine whether they are satisfied or (b)the policy analyst is convinced that any policies implied by his model are better than anything else that could be done even if the model is inapplicable in the prevailing circumstances. Well known theorems and results from the economics literature are then shown to imply that the conditions of application of the economic models used in integrated assessment analysis are so implausible as to render those models useless in that (or probably any) policy context. A constructive alternative programme for integrated assessment is then proposed.