Climate Change - Science topic
Climate Change is an any significant change in measures of climate (such as temperature, precipitation, or wind) lasting for an extended period (decades or longer). It may result from natural factors such as changes in the sun's intensity, natural processes within the climate system such as changes in ocean circulation, or human activities.
Questions related to Climate Change
looking for a model, forecasting the likelihood and impact of volcano eruption risk, due to climate change (glacier melting, destabilization, etc.).
As climate change hits the affected areas almost at the same time, this may be an interconnected cluster risk across the globe?
Seems the IPCC report does not quantify some Non-Noise risk for this in the RCP Scenarios.
Cherish research and ideas.
I am going to derive the precipitation data from NETCDF files of CMIP5 GCMs in order to forecast precipitation after doing Bias Correction with Quantile Mapping as a downscaling method. In the literature that some of the bests are attached, the nearest neighborhood and Inverse Distance Method are highly recommended.
The nearest neighbour give the average value of the grid to each point located in the grid as a simple method. According to the attached paper (drought-assessment-based-on-m...) the author claimed that the NN method is better than other methods such as IDM because:
"The major reason is that we intended to preserve the
original climate signal of the GCMs even though the large grid spacing.
Involving more GCM grid cell data on the interpolation procedure
(as in Inverse Distance Weighting–IDW) may result to significant
information dilution, or signal cancellation between two or more grid
cell data from GCM outputs."
But in my opinion maybe the IDM is a better choice because I think as the estimates of subgrid-scale values are generally not provided and the other attached paper (1-s2.0-S00221...) is a good instance for its efficiency.
I would appreciate if someone can answer this question with an evidence. Which interpolation method do you recommend for interpolation of GCM cmip5 outputs?
Thank you in advance.
I got two climate datasets containing data of mean anual air temperature for my study area, one based on interpolation of near meteorological stations (CRU Time Series) and the other based in climate models (ERA5), because of the absence of meteorological stations in the area. On both of them, despite wich kind of fit i try to do, R^2 values don't show values greater than 0,3. Also, i got climate data from a relatively close airport from a mining project, and the values show some linear trend, but R^2 is roughly 0,6. I tried autocorrelation too, but alaways values drop at lag-2. Am I correctly evaluating this? What maybe the reason why i can't see the warming pattern? I'm not a climatologist, but this kind of insights reach closely my topic, that is criotic high altitude permafrost related processes, and it's evaluation in time. I thank you in advance for your time and consideration on this matter.
My Objective is to simulate the impact of climate change on maize yields in Isingiro District under future climate scenarios. 0.8435° S, and 30.8039° E
Where can I get the above R Scrit for simulation for RCP4.5 and RCP 8.5 from?
Will be greatful if any one who has can share with me
Those who read the 1987 Brundtland Commission Report know that it was about sustainable development solutions to the social and environmental sustainability issues embedded in the traditional market model due to the assumption of social and environmental externality neutrality that had led to social problems(poverty, over population) and environmental problems(Pollution, environmental degradation) that the commission highlighted as the reason for the need to go, not half way from business as usual, but away from business as usual, and they gave us the definition of sustainable development, not of sustainability…..
But look at the UN related page below and its content:
Sustainable development requires an integrated approach that takes into consideration environmental concerns along with economic development.
In 1987, the United Nations Brundtland Commission defined sustainability as “meeting the needs of the present without compromising the ability of future generations to meet their own needs.” Today, there are almost 140 developing countries in the world seeking ways of meeting their development needs, but with the increasing threat of climate change, concrete efforts must be made to ensure development today does not negatively affect future generations.
The Sustainable Development Goals form the framework for improving the lives of populations around the world and mitigating the hazardous man-made effects of climate change. SDG 13: Climate Action, calls for integrating measures to prevent climate change within development frameworks. SDG 14: Life Below Water, and SDG 15: Life on Land, also call for more sustainable practices in using the earth’s natural resources. “
See we know, a) sustainability(optimization based) is not sustainable development (maximization based); b) The commission gave us a definition of sustainable development and not of sustainability as they saw the social and environmental issues created by the traditional market in terms of sustainable development thinking; c) that is why we have sustainable development goals, NOT sustainability goals.
We know the sustainability model is different than the sustainable development model and according to the model inconsistency principle sustainability and sustainable development can not be equated or defined one as the other or the other as the one.
But the UN defines sustainability as sustainable development there, a scientific inconsistency as it violates the theory-practice consistency principle.
Which raises the question, Do defining sustainability as sustainable development requires alternative academic facts? If yes, Why?
I think YES, what do you think?
Feel free to provide your own view when answering the question.
Climate change become increasingly an important global issue. How can we effectively mitigate its negative impact and make a stable growth in the world economy?
I came across a lot of peer-reviewed journal articles and most of the authors have concluded there is a climate change phenomena happening by applying Mann-Kendall Trend test on Hydro-Metrological variables (rainfall, temperature). It has to noted that Mann-Kendall is a statistical technique which on applied to dataset (including time series) shows whether there is a monotonic increasing or decreasing trend & whether that trend so arrived is statistically significant or not ?
My question is that how we can conclude the trend detected is due to climate change only without citing any physical process/phenomena (like Teleconnections) drives this change ?, that too based on Statistical test (Mann-Kendall) at a particular Level of significance (LOS).
The LOS applied is also statistically subjective and the value can vary from person to person?
I'm very keening to obtain more information of how climate change affect the storm water pollution especially in urban areas with various articles published may be very helpful to my project.
When you look at discussions about human population, whether from the overpopulation point of view in particular or population dynamics view in general, they lead to policy actions and recommendations that appear to be independent of the traditional market structure structure(price, consumption, and production) that supports them, but the nature of markets seems to shape the nature of the population and population dynamics they encourage.
And this raises the relevant question once and for all:
Is the nature of human population dynamics dependent or independent of the nature of the traditional market structure dynamics that serves them?
I think that the nature of the population and its dynamics is dependent of the nature of the markets that serves them as they shape their nature, what do you think?
Are they independent? Yes or No, and why do you think so?
Are they dependent? Yes or No, and why do you think so?
What do you think?
Forest plays a great role in the domain of climate change. Field instruments are very important for studying forest ecology (Kindly see the VDO attached)
In my country, more than a dozen years ago or more, there were real winters with snow and frost after the autumn. Whereas last winter, during the last few years it looked like autumn, without snow and positive temperatures. I think that the greenhouse effect, ie the warming of the Earth's climate, has already begun. This is also confirmed by numerous climatic cataclysms and weather anomalies, which in the current year 2018 appear in numerous places on the Earth. In some parts of the Earth there are fires of huge forest areas such as in Scandinavia, California in the USA, Australia, the Iberian Peninsula, Africa, etc. In addition, weather anomalies, e.g. snow and floods in October and November in the south of Europe.
In addition, tornadoes in many places on Earth and so on.
Perhaps these problems will get worse. It is necessary to improve security systems and anti-crisis services, improve the prediction of these anomalies and climatic cataclysms so that people can, have managed to shelter or cope with the imminent cataclysm. One of the technologies that can help in more precise forecasting of these cataclysms is the processing of large collections of historical and current information on this subject in the cloud computing technology in Big Data database systems.
Therefore, I am asking you: Will new data processing technologies in Big Data database systems allow for accurate prediction of climate disasters?
Please, answer, comments. I invite you to the discussion.
Forests are the biodiversity wealth of natural ecosystems and a key factor in the wealth of the planet's biosphere. However, this natural wealth is rapidly being eroded by human civilisational activities. The scale of forest fires has been increasing in recent years. The increasing scale of forest fires is a result of the ongoing process of global warming. In some regions of the world, forests are also being burned in order to acquire more land for the cultivation of agricultural crops, which is usually carried out under predatory and unsustainable farming practices. It is well known that forests are one of the key factors in reducing the rate of increasing CO2 in the atmosphere, an important factor in slowing down the greenhouse effect and consequently also in slowing down global warming. It is therefore essential to increase the scale of forest fire protection.
The following questions are therefore becoming increasingly topical:
How to protect forests from fires?
What is your opinion on this subject?
What do you think about this topic?
I invite you all to discuss,
Thank you very much,
would like to learn more about the development of camel meat and dairy farming and its impact on the eco-balance (CO₂, Methane, and water consumption) in comparison to the traditional cattle industry?
Has it got the potential to disrupt the cattle industry, given the climate path ahead?
Cherish your feedback.
We are preparing a paper on climate change and migration, and have some budget earmarked for open access fees. However, the money needs to be spent in this calendar year. The journal we wanted to submit to can only send an invoice after the article has been accepted. And it usually takes a while before the reviews are in and the revision done. Hence my question: Does anyone know a suitable journal (topic-wise and with good impact) that allows paying open access fee before an article is accepted? Or does anybody have other advice that can be of use here?
Thank you and best wishes
How can the pro-environmental transformation of agriculture be accelerated?
Agriculture is likely to change significantly in the future. Key determinants of agricultural change include factors such as climate change, including the progressive process of global warming. On the one hand, the main negative effects of the progressive global warming process include increasingly frequent and severe periods of heat and drought. In addition, by the end of this 21st century, all glaciers will have melted, water levels in the seas and oceans will have risen, and areas of arable land will have declined. On the other hand, in order to slow down the progressive process of global warming, it is necessary to urgently reduce the level of greenhouse gas emissions, including CO2 and methane. Climate change will cause a reduction in the scale of agricultural crop production. In order to feed the population, it will therefore be necessary to change dietary habits by switching agricultural crops to the production of plant-based agricultural crops to produce food primarily for humans rather than livestock. At present, globally, three quarters of arable land is used for the production of arable crops, from which fodder is produced to feed reared livestock. In addition to this, due to increasing global warming processes, it will be necessary to develop new crop varieties that are resistant to various biotic (viral, bacterial, fungal diseases, pests, etc.) and abiotic (droughts, heat, floods and other effects of climate change) negative environmental factors. In addition to this, the importance of achieving sustainability goals in agriculture will increase in the future. The generation of energy used in agriculture from renewable and emission-free energy sources will increase in importance. Consequently, environmentally neutral, emission-free, sustainable organic farming based mainly on crop production using new crop varieties that are resistant to progressive climate change and its effects will be developed in the future.
In view of the above, the following questions are becoming increasingly topical:
How should sustainable organic farming be developed?
How should sustainable, pro-environmental organic farming be developed?
How should environmentally neutral, emission-free, sustainable organic farming be developed?
What kind of agriculture should be developed to save humanity from a food crisis?
What type of agriculture should be developed to be environmentally neutral, meet sustainability goals, be resilient to climate change and provide food for people?
Is a combination of pro-environmental, sustainable organic agriculture and the development of new climate-resilient crop varieties the best solution?
How can the pro-environmental transformation of agriculture be accelerated?
What do you think about this?
What is your opinion on this topic?
I invite you all to discuss,
Thank you very much,
To what extent will the current uncertainties about fossil fuel energy supplies encourage or discourage the exploration for alternative energy sources. Given the abnormal climate occurrences over the past two decades, will this help to refocus minds and what might be the impact on countries for whom fossil fuels make up a substantial part of their GDP?
I would like to make as broad as possible an inventory of all indices and indicators of vulnerability to climate change. Can you point me to articles or indicators that you know of to help me with this task !?
Thanks in advance !
There is a new term in used today “Climate change economics”, and this seems to imply the existence of an environmentally friendly economic thinking, which raises the question is climate change economics green economics?
What do you think?
Please try to provide your own view on the answer to this question
This is an academic question posted in good faith to exchange ideas
I am looking for three climatic parameters that I can identify and extract from satellite imagery for a research study titled "Impacts of urbanization on local climate change". All parameters should be in numerical form so that the correlation between them can be analyzed and local climate variation can be observed.
I am doing my research based on Indigenous knowledge, skills, tool, and techniques using cassava as the main crop to address climate change and food security in Papua New Guinean rural communities.
if you know of any websites to search or any articles to read or even better, if you've done some research on this topic and would to give me some tips, please let me know.
I am currently working to prepare a research proposal on AI, Machine learning and climate change. I want to create a link b/w machine learning and climate change and find an idea to reduce GHG emissions via machine learning and AI. Can anyone from this field help me. I would be highly obliged.
I am interested in a discussion about the calculation of curves of Return Period for risk analysis (for natural phenomena).
I know that most of the building construction standards calculate these curves assuming stationary conditions; in other words, they do not consider the impact of climate change. What is their justification for using stationary conditions? Could some colleagues please send me links to papers, reports, etc. about this issue? Thank you very much.
How to choose wisely the journal (Indexed Scopus) in which to publish your first article. I work on household vulnerability assessment to climate change.
Are there any tools to find out the reaction time of journals after submission?
Thank you in advance for your answers!
The new scenario family that IPCC is using in its new reports is a combination of Representative Concentration Pathways (RCPs) and Shared Socio-economic Pathways (SSPs). You can read more about them at these papers:
As there is no probability assigned to different scenarios, scenario users are left with a numerous scenarios projecting the future world regarding the emission (RCPs) and societal and economic conditions (SSPs).
When you are planning to adapt to climate change, the impacts differ with each scenario (SSP-RCP combination. For example, planning for adaptation when you are facing a condition like SSP1-RCP2.6 is much cheaper, easier and achievable compared to a high emission scenario like SSP5-8.5 (combined with a socio-economical condition making adaptation challenging).
The discussion that I'm trying to open here is about the approaches you use / assumption you make in your studies when working with these scenarios. As my main audience is the people who use scenarios, I'll be grateful if you could share this post with people working in these areas.
Research Assistant at University of Waterloo
PhD System Design Engineering
In the advent of climate change, conditions suitable for local species could be significantly altered. Hence, planting characteristic tree species of the planting sites may not be feasible. There are several pieces of literature recommending the use of composite provenance in order to restore climate-resilient characteristic tree species/forests. However, the issue of outbreeding depression is a concern. So, my question is: in the advent of climate change, would it be wise to use planting material from composite provenance for forest restoration?
Do you agree with me that we need to take care of green building and sustainable development in light of climate changes?
I used lars-wg6 to assess climate change for the years 2021_2040, the annual average precipitation during theses years shows 99percent decrease comparing to the observed data from 1984_2016.
Is this much decrease even possible?
Does anyone know where I might have made a mistake? Because I changed so many things yet the amounts are about the same.
Many studies on climate change and environmental sustainability have established a positive relationship between population size and environmental pressure. Is it, however possible for the population growth of neighbouring countries to cause a decline in CO2 emissions in a country, taking the spatial relationship among countries into consideration?
If I have multiple scenarios with multiple variables changing, and I want to conduct a full factorial analysis, how do I graphically show the results?
Will the world face a global water crisis in light of the increasing climate change?What are the most important international measures needed to confront the danger of this crisis?
What kind of scientific research dominate in the field of Global warming?
Please, provide your suggestions for a question, problem or research thesis in the issues: Global warming.
I invite you to the discussion
Excessive use of chemicals in agriculture results in contamination of products with high levels of chemical residues.
What are the obligations of states with regard to human rights in the face of increasing climate change?
Should our collective focus orient more toward humans’ capacity for adaptation?
When mitigation is still an option, why would we not act to ensure less severe negative effects?
How mitigating models and systems can exceed and overtake adaptive strategies in dealing with climate change?
Environmental problem often links with complex system. For example, climate change issues are related to global climate system, ecosystem and human society.
Complexity science is a discipline that study complex system. The application of complexity science might bring new inspiration and powerful tool in solving environmental problem.
What are the environmental problems that complexity science might be useful in solving it?
I have long-term rainfall data and have calculated Mann-Kendall test statistics using the XLSTAT trial version ( addon in MS word). There is an option for asymptotic and continuity correction in XLSTAT drop-down menu.
- What does the term "Asymptotic" and "continuity correction" mean?
- When and under what circumstances should we apply it?
- Is there any assumption on time series before applying it?
- What are the advantages and limitations of these two processes?
What are the following things called ?
Are they addressed as Climate Indices or Teleconnection ?
- North Atlantic Oscillation (NAO)
- East Atlantic West Pacific Oscillation (EAWP)
- Scandinavian Pattern (SCP)
- Niño 3.4 (the region encompassing 5°N–5°S, 120°W–170°W)
- Pacific Decadal Oscillation (PDO)
- Arctic Oscillation (AO)
- Antarctic Oscillation (AnO)
- Southern Oscillation Index (SOI)
- Dipole Mode Index
What is the usefulness of the above-mentioned climatic indices? From where can we download these indices ? How these climatic indices are related to Climate Change ? If there any research article explaining the fundamentals and giving step by step methodology there on how to relate these indices with climate change ?
recently, all the globe is talking about climate change and its effect, so i would like to make a research article about climate change and its relation with drug residues in food of animal origin starting from drug administration to animals
I have received data for 50+ rainfall stations in my study area. Is there any mathematical formulae which gives optimum number of rainfall stations for trend based climate change study ? All of them have missing values in them, ranging from 1% to 50%. Upto what percentage of missing values can be filled by statistical methods and how to determine the threshold limit (whether 10% or 15% or 20%). If any literature is available on this, kindly intimate me
For instance, i want to use the IPCC 2013 method for the impact category climate change, but use Usetox for ecotoxicity and CML-IA baseline 2013 for eutrophication and resource deplition (fossil fuels). Is this possible? How do i make a method that incorporates all the different already existing methods?
There is a lot of demand for information about the potential of different tree species (and also other types of crops) to capture and store carbon.
Such information is critical to designing farms with a better climate footprint.
This is particularly true for agroforestry farming systems relying on one or more tree species!
However, information on this topic seems completely scattered and not synthesized.
Does such a database exist?
Do you agree with me that we have to combat desertification in each of its locations in order to reduce severe climate changes and dust ؟
The goal of shifting from pollution based markets to clean markets is affected by going green markets and by going dwarf green markets in opposing ways.
The working of green markets moves away from pollution based markets and it tends towards clean markets while the working of dwarf green markets stays far away from clean markets and very close to pollution based markets.
Which raises the question, What are the clean market consequences of green market paradigm shift avoidance?
What do you think?
Please try to answer the question first, and then make any comments you think are appropriate.
And I will reply.
In view of the recurring droughts in recent years because of climate change. We have an idea to establish a national project to confront climate change in Iraq.
Air pollution and climate change are unstable or even very bad, causing many lives to be affected by cancer, heart, lung, malnutrition, malaria, diarrhea, and heat stress.
What role can we (as in health) play in dealing with this?
Electrical vehicles have been touted as one of the major ways to combat global climate change. Without combustion of fossil fuel producing gases harmful for human health is a major step towards using electric vehicles but it requires some drastic steps to replace the gas fired vehicles. The cost of vehicles and the lack of supercharging stations are some of the obstacles need to be overcome before making it a potential solution. Unfortunately, this type vehicles are not yet available in many countries. What are your comments?
I am researching the recovery of sandy soils (Arenosols) affected by desertification due to climate change. Recovery methods do not include irrigation. What methods have you tried and worked out?
I plan to research the impacts (climatic and man made) of mangrove on fishery stocks. It would be wonderful if I could get some of your expert advice to make my research more precise and accurate.
I am looking for a way to build land-use models for the future 2050 and 2100. In addition to social and economic variables, I would like to include in these models the effects of climate change following the scenarios of SSPs 4.5 and 8.5. Do you have any suggestions for me? What are the limitations of these models?
My PhD research proposal is to determine the impact of climate change on farmers in the different agro-climatic zone of West-Bengal. I am quite confused to frame the sample size as I am not getting the proper theory to fit against the farming population of Bengal.