Climate Modeling - Science topic

In the EC-Earth3-Veg GCM, there are variables such as air_temperature and surface_air_temperature. What is the difference between these two variables in terms of how they are represented and used in climate modeling? Additionally, do these variables represent the actual temperature or temperature anomalies? Understanding their specific roles will help clarify their application in my climate studies.

I plan to use CMIP6 data, specifically from the EC-Earth3-Veg GCM (for historical runs and all SSPs), for climate modeling. However, I need clarification on the variables such as precipitation_flux, air_temperature, surface_air_temperature, and others. Is there a comprehensive document that lists all the variables used in the model, along with their descriptions, units, and any scaling factors?

Climate Change [GHGs; CO2 Concentration]

1. Are glacial cycles are ultimately paced by astronomical forcing?

2. Role of CO2 in glacial cycles: Were the concentrations of GHGs were both increasing as well as decreasing over the last glacial cycle?

3. Whether CO2 remained to be the primary driver of the ice-ages?

4. Whether CO2 remains to be largely a consequence rather than cause of past climate change?

5. Whether climate models have also predicted the dominant contribution of GHGs (apart from ice albedo) to ice-age cooling?

6. Whether global temperature closely tracked the enhancement in CO2 concentration over the last deglaciation? CO2 did not initiate deglacial warming?

Suresh Kumar Govindarajan

Professor (HAG) IIT Madras

27-July-2024

I have observed in some cases there are large negative values of surface geopotential in Era5 and MERRA2, in most cases in regions with complex orography. Why this happens? How can i handle this?

Monitoring climate change requires accurate and reliable atmospheric data to understand and predict changes in weather patterns, temperature, and other critical variables. This question aims to identify specific sources or platforms that provide atmospheric data essential for climate change research and monitoring. It seeks to explore the most trusted and widely used data sources, such as satellite missions, ground-based observation networks, and climate models, and to understand their strengths, limitations, and applications in climate science. The goal is to provide researchers and policymakers with information on where to obtain high-quality atmospheric data to support their efforts in studying and mitigating the impacts of climate change.

AI has immense potential to address various aspects of climate change by enhancing our understanding of the climate system, optimizing resource usage, and developing innovative solutions. Here are several ways AI can contribute:

https://youtu.be/IU73L5zty7s?si=YNbHhYdZofNpdGDU is giving a direction

Hello!

I have been modelling the distributions of two orchid species across the island of Puerto Rico, and I have received very promising results from Maxent's output of their current distributions (AUC values around 0.90). I used Worldclim variables, elevation, and land use as my environmental layers.

The next step in the project is projecting this output against future climate scenarios for the island. I have been using the GCM database at http://www.ccafs-climate.org/data_spatial_downscaling/ since they also used the bioclim variables. So far, I have only modeled their distributions against one climate model, and the output did not incite much confidence to stand on it's own (RCP2.6 had very similar results to RCP8.5 by 2050). Due to the inaccuracies and uncertainties of downscaled climate projections across fine-scaled, small spatial areas, I am thinking that I should use multiple climate models for this project.

However, I am unsure how many different climate models I should use to adequately represent the distribution possibilities on the island. What are some general practices/experiences that people might recommend for this situation?

Also I have these questions:

2.What king of level spacing should we employ above the PBL for such studies?

3.Also what should be the model top height for such studies? Is it really necessary to go above the tropopause.

How can generative AI contribute to solving real-world problems, such as healthcare diagnostics or climate modeling?

I have tried with no avail to make a logistic regression and random forest model work.

First i fitted a model with climatic measurements as independent variables (2015-2020, almost five years) and mosquito incidence (coded binary for a n regions) data as target. There were some under-sampling issues that were dealt with and the overall models were significant. Some variables were not-significant (or non important for RF) and not kept in the model.

Then, the same (significant) variables were extracted from a future time climate model for 2080-2085, and used to predict the incidence for said region.

The problem is that it for any year on the future time period it predicts presence for all regions.

Anyone has dealt with problems like this? logistic regression or RF models being inconclusive or unusable when used for prediction?

Best

Another security line has been crossed in 2023. The increase in average global temperature will reach 1.4 degrees Celsius above the level of the Industrial Revolution this year, the World Meteorological Organization is warning.

This year's preliminary report by the World Meteorological Organization (WMO), titled "State of the Global Climate," confirms that 2023 will be the warmest year on record for measurements, surpassing the so far record-breaking year of 2016, when an increase of 1.2 degrees Celsius in average global temperature was recorded. This signals that the world, despite declarations to the contrary, is moving away from its stated goals for action to limit temperature increases. Climatology research shows that there has been a record increase in average atmospheric temperature in the global scla in 2023. The World Meteorological Organization has published a report presenting the results of research conducted on long-term climate change analysis. The publication of this report took place in early December 2023, i.e. at the beginning of the UN Climate Summit COP28 in Dubai.

The crossing of the aforementioned next security limit confirming the thesis of the increasing scale of the natural and climate catastrophe unequivocally means that the declarations made by the leaders of the world's leading countries on combating climate change at the UN Climate COP, among others, do not fully coincide with actions and the actions taken are still far too limited. Unfortunately, individual countries are not meeting their declared targets for reducing greenhouse gas emissions to a sufficient degree. The recent UN Climate Summit COP28 in Dubai indicates that this does not look like it will change any time soon. WMO Secretary-General Peterri Taalas, commenting on the theses of a report prepared by the organization he heads, said, "Greenhouse gas emissions are at record levels. The average global temperature is at record levels, water levels in the oceans and seas are rising, the Antarctic ice sheet is at record thinness."

WMO Secretary-General Peterri Taalas also announced that "these are not just statistics. We risk losing the battle to save the glaciers and halt the rise in sea and ocean levels. We can't go back to the 20th century, but we must act today to reduce the risk of a climate that is very unfavorable to life." In support of these words, the report's authors point out that in 2023, the Arctic's ice cap area is only one million square kilometers, less than the previous infamous record in this category. In addition, glaciers in the Swiss Alps have shrunk by 10 percent in the past two years. The scale of forest fires has increased. Forest fires in Canada that occurred in 2023 covered a record 5 percent of the country's total forested areas. These are just some examples of events that are related to climate change. But from the aforementioned report there is also a small spark of hope for humanity and the planet's biosphere.

Well, the conclusions of the authors of the WMO report, however, do not mean that we have already permanently crossed the threshold of a 1.5-degree increase in average temperature, which the Paris Agreement defined as a level that means catastrophic and irreversible global climate change. On the other hand, however, it is a threatening prediction, a highly probable long-term forecast of what may happen in the near future. Besides, what until recently was referred to as a long-term prediction of how climate change will shape up over the next few decades is likely to take place over a much shorter period, as the processes of climate change, including the progressive process of global warming, have accelerated significantly in recent years.

According to data reported by Reuters, there are many indications that 2024 may see further records of the planet's atmospheric temperature increase. As a result, 2024 could be another year of records in terms of changes in average global temperature. One of the additional factors intensifying the magnitude of the planet's temperature increase in 2024 is the phenomenon known as El Nino, i.e. the phenomenon of the persistence of above-average surface temperatures in the equatorial zone of the Pacific. Well, during the operation of the El Nino phenomenon, large amounts of energy flow from the ocean to the atmosphere, causing a short-term increase in the average temperature of the Earth's surface and increasing the likelihood of a year with record high average temperatures. The full release of the State of the Global Climate 2023 report is expected in the first half of 2024.

In view of the above, there is not much time left to save the planet's climate, biosphere and biodiversity. To what extent it will be possible to save the climate, biosphere and biodiversity of the planet's natural ecosystems for future generations depends on how quickly and efficiently the green transformation of the economy can be carried out, including the green transformation of the energy sector, and succeed in building a green, zero-emission, sustainable closed-loop economy.

I am conducting research on this issue. I have described key aspects of the above issue in the article: IMPLEMENTATION OF THE PRINCIPLES OF SUSTAINABLE ECONOMY DEVELOPMENT AS A KEY ELEMENT OF THE PRO-ECOLOGICAL TRANSFORMATION OF THE ECONOMY TOWARDS GREEN ECONOMY AND CIRCULAR ECONOMY

I invite you to scientific cooperation in this problematic,

In view of the above, I address the following question to the esteemed community of scientists and researchers:

And what is your opinion on this topic?

What is your opinion on this issue?

Please answer,

I invite everyone to join the discussion,

Thank you very much,

Best regards,

Dariusz Prokopowicz

I do not doubt climate change and the disasters that come and will come from it ( ). However, one thing puzzles me: why would melting ice lead to rising ocean water levels? Plain high-school physics tell us it would not: see, for example: https://lnkd.in/d78U9S_f.

Of course, there is another physical thing we've all learnt in high school: warmer substances have a larger volume than cold substances. However, we also know the volume expansion coefficient of water is very tiny: only 210 parts on a million per degree Celsius (https://lnkd.in/e65sxJ5a). Oceans are 3.8 km deep (on average) but span millions of km2, so that can explain a few cm only - at best. Also, studies on rising sea levels in coastal cities show these cities tend to sink. So they need better shore protection but it has got nothing to do with rising ocean levels, it would seem.

Any thoughts, anyone? [Again, I am not a climate change denier. See my rant against John Clauser, for example: https://readingfeynman.org/2023/09/04/another-tainted-nobel-prize/.]

Hey everyone!

Where can I find future projections of global climate, preferably decadal average forecasts (means to yr 2030, 2040, ..., 2100) in a WorldClim-like format (bioclimatic predictions for different climate models).

Many thanks,

Lilian

A growing number of research centers analyzing the planet's climate in the long term, analyzing the progressive process of climate change, developing long-term forecast models of climate change, changes in ocean water temps are publishing the results of their research, which show that as a result of human civilizational activity, as a result of still increasing greenhouse gas emissions, the process of global warming in recent years has accelerated faster than previously predicted just a few years ago. These institutions include, among others, the international research team established at the UN and publishing IPCC reports. In addition to this, the European Space Agency (ESA) Copernicus also recently published the results of its ongoing research on the climate of planet Earth, which showed that in the 1st half of 2023, the average temp. of planet Earth's atmosphere was 16.8 degrees C. This is the highest temp. in the history of measurements. This is further evidence supporting the thesis that the global climate crisis has begun, and that the green economic transformation measures carried out in recent years, including the reduction of greenhouse gas emissions, were definitely insufficient. In addition, in many countries, these actions have fallen far short of the pledges made at the UN Climate COP. Besides, the results of the aforementioned studies also support the thesis that the process of global warming in recent years has accelerated faster than previously predicted. This is a particularly important issue in the context of the living conditions of the next generations of people on the planet. Living conditions will rapidly deteriorate for many people on the planet in the not too distant future. Increasing summer heat, droughts, forest fires, weather anomalies, violent storms, drinking water shortages, deterioration of air quality, melting glaciers, rising water levels in the seas and oceans, shrinking areas of forests and other types of natural ecosystems, deterioration of the natural environment, progressive loss of biodiversity of natural ecosystems, extinction of pollinating insects and many other forms of life, etc. these are the key effects of the progressive global warming process, which will determine the deterioration of the quality of life on the planet for many people. In 2023 and 2024, the El Ninio effect is also an additional factor generating an increase in atmospheric temperature. However, according to the results of studies on the planet's climate, analyses of long-term climate change El Ninio is only an additional factor to the main factor is still the rapidly increasing greenhouse gas emissions generated by human civilization still based largely on the dirty combustion economy. However, there are many more research results also conducted by other institutions and research centers confirming the above theses. Please also provide other results of research conducted on this issue.

In view of the above, I address the following question to the esteemed community of scientists and researchers:

Please answer,

I invite everyone to join the discussion,

Thank you very much,

The above text is entirely my own work written by me on the basis of my research.

In writing this text I did not use other sources or automatic text generation systems.

Copyright by Dariusz Prokopowicz

On my profile of the Research Gate portal, you can find several publications on the issues of environmental policy, green transformation of the economy, green economics, sustainable economic development, etc. I invite you to scientific cooperation in these issues.

Best wishes,

Dariusz Prokopowicz

Are there any researchers or organizations interested in collaborating on enhancing our understanding and modeling of complex natural phenomena, such as ocean currents, atmospheric flows, and geophysical fluid dynamics, with the goal of advancing weather prediction, climate modeling, and promoting environmental sustainability? Please reach out if you are interested in collaborating on this important research area.

Hi everyone!

I'm searching for forecast data (netcdf or grib) for the next few months, but I've found only CFSv2 (https://nomads.ncep.noaa.gov/pub/data/nccf/com/cfs/prod/) and NMME (https://ftp.cpc.ncep.noaa.gov/NMME/realtime_anom/ENSMEAN/).

Does anyone know another source for this kind of forecast?

PS.: I know that NMME is multimode, but I was searching for more options either way.

Hello,

The terms on the RHS represent the subgrid-scale, and the ones on the LHS represent the large scale, yet both are coarse-grained-averaged. So why do the terms on the RHS still represent the subgrid scale?

Thanks

My question might sound bit silly. But, I am really curious to know what makes two GCMs differnt? We know, GCMs simulate past and future climate based on some assumptions. Regarding future, we can not comment anything, but when the task is to simulate past climate, why does the model output vary from each other? Can someone please elaborate on this with some examples?

How the proxy data (from lacustrine records) are useful to validate climate models for future climate predictions?

Any suggestion would be great.

Thank you

Please, I need, if available, some important research papers which relate the theory of dynamical systems to climate change. Also, in general, I know there are a lot of published research articles that relate dynamical systems to many applications. But, are there papers that research centers and governments depend on that before taking any procedures? I mean, are there papers, especially on climate change and the environment, which are not only in theory but have practical applications?

How to do data assimilation with a regional climate model (ICTP RegCM)?

All significant climate change studies have been properly studied and are based on the most current, reliable science. Climate models form a reliable guide to potential climate change. However, which data is best for analyzing a certain area's climatic trend remains unclear. Climate data from weather stations or NASA's Worldwide Energy Resource Prediction (POWER).

I recently acquired subsets of two climate datasets containing data of monthly mean air temperature for my study area, one based on interpolation of surrounding meteorological stations and the other based on climate models. On both of them, despite wich kind of fit i try to do, the coefficient of determination values are below 0.3. Comparing this data with climate data from a relatively close airport, the values show some linear trend, but coefficient of determination is 0.65 and the slope has an opposite sign. What maybe the reason of this apparent local absence of trends? I thank you in advance for your time and consideration on this matter. (On the graphs: y-axis degree celsius, x-axis years).

hi

please guide

how to make multi model ensemble of regional climate model?

I am using south asia domain of cordex and my variables are precipitation, tmax and tmin.

there exists 153 different combinations for these three variables of historical, rcp 4.5 and rcp8.5 scenarios.

how to shortlist models and then how to proceed?

So, which CMIP6 models are suitable for the case of East Africa, specifically, Ethiopia?

Please, I need suggestions for the appropriate model? Any scholar/researcher with experience in climate change modelling can give me constructive suggestions or feedback on this?

I thank you for your suggestion!

Currently, on CHELSA there are 5 models of future projection available. How to choose the best 3 of them? Are there any parameters that should be prefered when performing SDM on MAXENT (bioclim data)?

There are two parameters that I think may affect the reliability of MAXENT output. ECS (equilibrium climate sensitivity) or TCR (transient climate response). But I am not completely sure about it.

Any kind of help and suggestion would be greatly appreciated.

I am trying to choose models for my Species Distribution Analyses. I know that according to CMIP6, up to 100 models should be released. However, I found only 68 in Meehl et al. 2020. Moreover, there are only five models available to download from the Chelsa database. I wonder how I can prefer one model to another? According to ECS (equilibrium climate sensitivity) or TCR (transient climate response)? If yes, which one is crucial in choosing the model?

Extended/edited from an early question for clarity.

I have temporally high resolution outputs of modelled climate data (model x, spanning 0-5000 ka. Low spatial resolution 0.5 degrees). Compared to other climate models, however, I have reason to believe it is under-predicting precipitation/temperature changes at certain time intervals. Is there a way to calibrate this with better quality records (i.e., those available via WorldClim/PaleoClim)?

For example, the response to the MIS 5e (120-130 ka BP) incursion of the African Summer Monsoon and Indian Summer Monsoon into the Saharan and Arabian deserts is very weak compared to the MIS 5e data from WorldClim/PaleoClim (and corroborated by palaeoclimatic data). Can I correct/calibrate model x with these more responsive models, and how should this be done?

Hi all,

Besides CESM LENS2, which modeling groups provide large ensemble experiments in the CMIP6 era?

Thanks

I have temporally high resolution modelled climate data (model x). Compared to other climate models, however, I have reason to believe it is under-predicting precipitation/temperature changes. Is there a way to calibrate this with better quality records (i.e., those available via WorldClim/PaleoClim)?

For example, the response to the MIS 5e (|120-130 ka BP) incursion of the African Summer Monsoon and Indian Summer Monsoon into the Saharan and Arabian deserts is very weak compared to the MIS 5e data from WorldClim/PaleoClim. Can I correct/calibrate model x with these more responsive models, and how should this be done?

I have seen in some discussion forums that climate projection models do not reliably reproduce twister phenomena. How can climate modeling overcome this challenge ?

I am looking for a Matlab toolbox/function for the visualization of climate projection to get the similar figure in the attachment.

Article of the figure:

Climate models are the energy balance model that accounts for feedbacks, natural variability, and an ocean to help simulate the main components that determine changes in global mean temperature.

Can we make ENSEMBLE data of Future temperature and precipitation from CMIP data (different models) available on the WORLDCLIM website?

I mean one dataset representing all the models for different SSP scenarios?

Regards

Gowhar Meraj

I read about CORDEX regional projections but I can't access the data files. Can someone suggest others regional climate models suitable for this area or provide a help on how to access the climate data for CORDEX. Thank you very much!

Hello,

"CDO" is a LINUX based operation for evaluating climate data.

Could any one assists me by means of installing or utilizing CDO on Windows operation systems?

Best regards

Saeideh Baghanian

Air-sea gas exchange is a physio-chemical process, primarily controlled by the air-sea difference in gas concentrations and the exchange coefficient, which determines how quickly a molecule of gas can move across the ocean-atmosphere boundary. It takes about one year to equilibrate CO2 in the surface ocean with atmospheric CO2, so it is not unusual to observe large air-sea differences in CO2 concentrations. Most of the differences are caused by variability in the oceans due to biology and ocean circulation. (Source: http://www.pmel.noaa.gov/co2/story/Ocean+Carbon+Uptake)

The threats that global warming has recently posed to humans in many parts of the world have led us to continue this debate.

So the main question is that what actions need to be taken to reduce the risk of climate warming?

Reducing greenhouse gases now seems an inevitable necessity.

In this part in addition to the aforementioned main question, other specific well-known subjects from previous discussion are revisited. Please support or refute the following arguments in a scientific manner.

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% ---------------- *** Updated Discussions of Global Warming (section 1) *** ---------------%

The rate of mean temperature of the earth has been increased almost twice with respect to 60 years ago, it is a fact (Goddard Institute for Space Studies, GISS, data). Still a few questions regarding physical processes associated with global warming remain unanswered or at least need more clarification. So the causes and prediction of this trend are open questions. The most common subjects are listed below:

1) "Greenhouse effect increases temperature of the earth, so we need to diminish emission of CO2 and other air pollutants." The logic behind this reasoning is that the effects of other factors like the sun's activity (solar wind contribution), earth rotation orbit, ocean CO2 uptake, volcanoes activities, etc are not as important as greenhous effect. Is the ocean passive in the aforementioned scenario?

2) Two major physical turbulent fluids, the oceans and the atmosphere, interacting with each other, each of them has different circulation timescale, for the oceans it is from year to millennia that affects heat exchange. It is not in equilibrium with sun instantaneously. For example the North Atlantic Ocean circulation is quasi-periodic with recurrence period of about 7 kyr. So the climate change always has occurred. Does the timescale of crucial players (NAO, AO, oceans, etc) affect the results?

3) Energy of the atmospheric system including absorption and re-emission is about 200 Watt/m2 ; the effect of CO2 is about how many percent to this budget ( 2% or more?), so does it have just a minor effect or not?

4) Climate system is a multi-factor process and there exists a natural modes of temperature variations. How anthropogenic CO2 emissions makes the natural temperature variations out of balance.

6) Some weather and climate models that are based on primitive equations are able to reproduce reliable results.  Are the available models able to predict future decadal variability exactly? How much is the uncertainty of the results. An increase in CO2 apparently leads in higher mean temperature value due to radiative transfer.

7) How is global warming related to extreme  weather events?

Some of the consequences of global warming are frequent rainfall, heat waves, and cyclones. If we accept  global warming as an effect of anthropogenic fossil fuels, how can we stop the increasing trend of temperature anomaly and switching to clean energies?

8) What are the roles of sun activities coupled with Milankovitch cycles?

9) What are the roles of politicians to alarm the danger of global warming? How much are scientists sensitive to these decisions?

10) How much is the CO2’s residence time in the atmosphere? To answer this question precisely, we need to know a good understanding of CO2 cycle.

11) Clean energy reduces toxic buildups and harmful smog in air and water. So, how much building renewable energy generation and demanding for clean energy is urgent?

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% ---------------- *** Discussions of Global Warming (section 2) *** ---------------%

Warming of the climate system in the recent decades is unequivocal; nevertheless, in addition to a few scientific articles that show the greenhouse gases and human activity as the main causes of global warming, still the debate is not over and some opponents claim that these effects have minor effects on human life. Some relevant topics/criticisms about global warming, causes, consequences, the UN’s Intergovernmental Panel on Climate Change (IPCC), etc are putting up for discussion and debate:

1) All the greenhouse gases (carbon dioxide, methane, nitrous oxide, chlorofluorocarbons (CFCs), hydro-fluorocarbons, including HCFCs and HFCs, and ozone) account for about a tenth of one percent of the atmosphere. Based on Stefan–Boltzmann law in basic physics, if you consider the earth with the earth's albedo (a measure of the reflectivity of a surface) in a thermal balance, that is: the power radiated from the earth in terms of its temperature = Solar flux at the earth's cross section, you get Te =(1-albedo)^0.25*Ts.*sqrt(Rs/(2*Rse)), where Te (Ts) is temperature at the surface of the earth (Sun), Rs: radius of the Sun, Rse: radius of the earth's orbit around the Sun. This simplified equation shows that Te depends on these four variables: albedo, Ts, Rs, Rse. Just 1% variation in the Sun's activity lead to variation of the earth's surface temperature by about half a degree.

1.1) Is the Sun's surface (photosphere layer) temperature (Ts) constant?

1.2) How much is the uncertainty in measuring the Sun's photosphere layer temperature?

1.3) Is solar irradiance spectrum universal?

1.4) Is the earth's orbit around the sun (Rse) constant?

1.5) Is the radius of the Sun (Rs) constant?

1.6) Is the largeness of albedo mostly because of clouds or the man-made greenhouse gases?

So the sensitivity of global mean temperature to variation of tracer gases is one of the main questions.

2) A favorable climate model essentially is a coupled non-linear chaotic system; that is, it is not appropriate for the long term future prediction of climate states. So which type of models are appropriate?

3) Dramatic temperature oscillations were possible within a human lifetime in the past. So there is nothing to worry about. What is wrong with the scientific method applied to extract temperature oscillations in the past from Greenland ice cores or shifts in types of pollen in lake beds?

4) IPCC Assessment Reports,

IPCC's reports are known as some of the reliable sources of climate change, although some minor shortcomings have been observed in them.

4.1) "What is Wrong With the IPCC? Proposals for a Radical Reform" (Ross McKitrick):

IPCC has provided a few climate-change Assessment Reports during last decades. Is a radical reform of IPCC necessary or we should take all the IPCC alarms seriously? What is wrong with Ross argument? The models that are used by IPCC already captured a few crudest features of climate change.

4.2) The sort of typical issues of IPCC reports:

- The summary reports focus on those findings that support the human interference theory.

- Some arguments are based on this assumption that the models account for most major sources of variation in the global mean temperature anomaly.

- "Correlation does not imply causation", in some Assessment Reports, results gained from correlation method instead of investigating the downstream effects of interventions or a double-blind controlled trial; however, the conclusions are with a level of reported uncertainty.

4.3) Nongovernmental International Panel on Climate Change (NIPCC) also has produced some massive reports to date.

4.4) Is the NIPCC a scientific or a politically biased panel? Can NIPCC climate reports be trusted?

4.5) What is wrong with their scientific methodology?

5) Changes in the earth's surface temperature cause changes in upper level cirrus and consequently radiative balance. So the climate system can increase its cooling processes by these types of feedbacks and adjust to imbalances.

6) What is your opinion about political intervention and its effect upon direction of research budget?

I really appreciate all the researchers who have had active participation with their constructive remarks in these discussion series.

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% ---------------- *** Discussions of Global Warming (section 3) *** ---------------%

In this part other specific well-known subjects are revisited. Please support or refute the following arguments in a scientific manner.

1) Still there is no convincing theorem, with a "very low range of uncertainty", to calculate the response of climate system in terms of the averaged global surface temperature anomalies with respect to the total feedback factors and greenhouse gases changes. In the classical formula applied in the models a small variation in positive feedbacks leads to a considerable changes in the response (temperature anomaly) while a big variation in negative feedbacks causes just small variations in the response.

2) NASA satellite data from the years 2000 through 2011 indicate the Earth's atmosphere is allowing far more heat to be emitted into space than computer models have predicted (i.e. Spencer and Braswell, 2011, DOI: 10.3390/rs3081603). Based on this research "the response of the climate system to an imposed radiative imbalance remains the largest source of uncertainty. It is concluded that atmospheric feedback diagnosis of the climate system remains an unsolved problem, due primarily to the inability to distinguish between radiative forcing and radiative feedback in satellite radiative budget observations." So the contribution of greenhouse gases to global warming is exaggerated in the models used by the U.N.’s Intergovernmental Panel on Climate Change (IPCC). What is wrong with this argument?

3) Ocean Acidification

Ocean acidification is one of the consequences of CO2 absorption in the water and a main cause of severe destabilising the entire oceanic food-chain.

4) The IPCC reports which are based on a range of model outputs suffer somehow from a range of uncertainty because the models are not able to implement appropriately a few large scale natural oscillations such as North Atlantic Oscillation, El Nino, Southern ocean oscillation, Arctic Oscillation, Pacific decadal oscillation, deep ocean circulations, Sun's surface temperature, etc. The problem with correlation between historical observations of the global averaged surface temperature anomalies with greenhouse gases forces is that it is not compared with all other natural sources of temperature variability. Nevertheless, IPCC has provided a probability for most statements. How the models can be improved more?

5) If we look at micro-physics of carbon dioxide, theoretically a certain amount of heat can be trapped in it as increased molecular kinetic energy by increasing vibrational and rotational motions of CO2, but nothing prevents it from escaping into space. During a specific relaxation time, the energetic carbon dioxide comes back to its rest statement.

6) As some alarmists claim there exists a scientific consensus among the scientists. Nevertheless, even if this claim is true, asking the scientists to vote on global warming because of human made greenhouse gases sources does not make sense because the scientific issues are not based on the consensus; indeed, appeal to majority/authority fallacy is not a scientific approach.

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% ---------------- *** Discussions of Global Warming (section 4) *** ---------------%

In this part in addition to new subjects, I have highlighted some of responses from previous sections for further discussion. Please leave you comments to support/weaken any of the following statements:

1) @Harry ten Brink recapitulated a summary of a proof that CO2 is such an important Greenhouse component/gas. Here is a summary of this argument:

"a) Satellites' instruments measure the radiation coming up from the Earth and Atmosphere.

b) The emission of CO2 at the maximum of the terrestrial radiation at 15 micrometer.

b1. The low amount of this radiation emitted upwards: means that "back-radiation" towards the Earth is high.

b2. Else said the emission is from a high altitude in the atmosphere and with more CO2 the emission is from an even higher altitude where it is cooler. That means that the emission upwards is less. This is called in meteorology a "forcing", because it implies that less radiation /energy is emitted back into space compared to the energy coming in from the sun.

The atmosphere warms so the energy out becomes equals the solar radiation coming in. Summary of the Greenhouse Effect."

At first glance, this reasoning seems plausible. It is based on these assumptions that the contribution of CO2 is not negligible and any other gases like N2O or Ozone has minor effect. The structure of this argument is supported by an article by Schmidt et al., 2010:

By using the Goddard Institute for Space Studies (GISS) ModelE radiation module, the authors claim that "water vapor is the dominant contributor (∼50% of the effect), followed by clouds (∼25%) and then CO2 with ∼20%. All other absorbers play only minor roles. In a doubled CO2 scenario, this allocation is essentially unchanged, even though the magnitude of the total greenhouse effect is significantly larger than the initial radiative forcing, underscoring the importance of feedbacks from water vapour and clouds to climate sensitivity."

The following notions probably will shed light on the aforementioned argument for better understanding the premises:

Q1) Is there any observational data to support the overall upward/downward IR radiation because of CO2?

Q2) How can we separate practically the contribution of water vapor from anthropogenic CO2?

Q3) What are the deficiencies of the (GISS) ModelE radiation module, if any?

Q4) Some facts, causes, data, etc relevant to this argument, which presented by NASA, strongly support this argument (see: https://climate.nasa.gov/evidence/)

Q5) Stebbins et al, (1994) showed that there exists "A STRONG INFRARED RADIATION FROM MOLECULAR NITROGEN IN THE NIGHT SKY" (thanks to @Brendan Godwin for mentioning about this paper). As more than 78% of the dry air contains nitrogen, so the contribution of this element is not negligible too.

2) The mean global temperature is not the best diagnostic to study the sensitivity to global forcing. Because given a change in this mean value, it is almost impossible to attribute it to global forcing. Zonal and meridional distribution of heat flux and temperature are not uniform on the earth, so although the mean temperature value is useful, we need a plausible map of spatial variation of temperature .

3) "The IPCC model outputs show that the equilibrium response of mean temperature to a doubling of CO2 is about 3C while by the other observational approaches this value is less than 1C." (R. Lindzen)

4) What is the role of the thermohaline circulation (THC) in global warming (or the other way around)? It is known that during Heinrich events and Dansgaard‐Oeschger (DO) millennial oscillations, the climate was subject to a number of rapid cooling and warming with a rate much more than what we see in recent decades. In the literature, these events were most probably associated with north-south shifts in convection location of the THC. The formation speed of North Atlantic Deep Water (NADW) affects northerly advection velocity of the warm subtropical waters that would normally heat/cool the atmosphere of Greenland and western Europe.

I really appreciate all the researchers who have participated in this discussion with their useful remarks, particularly Harry ten Brink, Filippo Maria Denaro, Tapan K. Sengupta, Jonathan David Sands, John Joseph Geibel, Aleš Kralj, Brendan Godwin, Ahmed Abdelhameed, Jorge Morales Pedraza, Amarildo de Oliveira Ferraz, Dimitris Poulos, William Sokeland, John M Wheeldon, Michael Brown, Joseph Tham, Paul Reed Hepperly, Frank Berninger, Patrice Poyet, Michael Sidiropoulos, Henrik Rasmus Andersen, and Boris Winterhalter.

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If we say that we have the data for daily mean temperature with a resolution of 1km x 1km. What is meant here by resolution "1km x 1km"?

Thanks in advance

Hi all,

I was thinking about applying the downscaling statistical methods on GPM using the MODIS cloud level 2 . However, I am not sure about it from the prospective of its validity on arid or semi arid areas with low number of observations for instance.

This is icbc part of the RegCM version 4

SVN Revision: exported compiled at: data : Mar 8 2015 time: 12:22:59

: this run start at : Tue Mar 10 04:39:07 2015

: it is submitted by : root

: it is running on : phy26

: in directory : /home/RCMsimulation

GLOBIDATE1 : 1981120100

GLOBIDATE2 : 2001010100

NSTEPS : 27885

Unknown dattyp

......I need help ...... .

Regards

I want to compare climate record of two different geographical regions in a specific time window (cca 8-6 ka BP). I guess that use of a suitable paleoclimate proxy based model, which work in spatial grid could be a solution. Any suggestion?

I wanted to ask that if we have to develop a modelling tool to anticipate the impacts of weather extreme events on the water quality of a lake but the amount of information collected in the field is scarce. What kind of models would be better to use and which are the natural processes we should include in the models. Please guide me briefly if possible.

Hi, everyone. I am recently very interested in the performance of the responses of the Coupled General Circulation Models (CGCM) like GFDL series (cold start) to some idealized wind-patchs. Such experiments have been done widely in the simple and intermediate air-sea coupled models. I am very curious about the results from the much more complicated models.

Has anyone done the similar experiments before or could you recommend me some published relevant articles? Thanks very much!

I want to run WRF - ARW model for a small domain of 100X100 km at a grid size of 3X3 km or smaller for forecasting weather parameters, which will be used as an input in a crop simulation model to forecast crop yield.

I need to purchase the required hardware and software for it but I'm very confused of the requirements and specifications which are required for the purpose.

I seek help to answer the following questions:

(1) What is the minimum and optimum H/W specification required and available in the market (preferably in India).

(2) What Linux OS Version and other compilers that would required to be installed?

Thanks

Vinay Sehgal

Indian Agricultural Research Institute, New Delhi

Hi,

Except synoptic stations, does anybody know any website/software that gives the climatological data of unequipped places?

I am aware of using interpolation methods, but, I am looking for a method that extract data for a desired location numerically (like an excel format etc,.).

Thanks,

Hey friends,

I am conducting a climate change study in an Australian region.

Does anyone know how to choose the best model for the region?

And moreover, say I want to do the assessment for wet, mean and dry conditions.

Should I just take the average daily rainfall as my reference to see which model has the wettest, driest or average prediction?

Cheers,

Hamideh

Hello

I am looking for an idea to study the effect of climate change on ozone pollutants using climate models. Please let me know if you know of any similar research.

best regards

Prof. H. Ertel generalized Rossby's work proposal of 1939. Prof. Rossby firstly proposed that instead of the full three-dimensional vorticity vector, the local vertical component of the absolute vorticity is the most important component for large-scale atmospheric flow.

Via an independent paper published in 1942, Prof. Ertel identifying a conserved quantity following the motion of an air parcel proved that a certain quantity called the Ertel potential vorticity is also conserved for an idealized continuous fluid.

https://en.wikipedia.org/wiki/Potential_vorticity

Several links to check on the topic powered by ResearchGate:

We need to prepare a weighted average multi-model ensemble of projected future daily precipitation by assigning weights to individual CMIP6 models based on past performance. For this purpose, We want to use Bayesian Model Averaging. Since the distribution of precipitation is highly skewed with large number of zeros in it, a mixed (discrete-gamma) distribution is preferred as the conditional PDF as per Sloughter et al., (2007).

Considering 'y' as the reference (observed ) data and 'fk' as the modelled data of kth model,

The conditional PDF consists of two parts. The first part estimates P(y=0|fk) using a logistic regression model. The second part consists the following the term P(y>0|fk)*g(y|fk).

Since the computation of P(y>0|fk) is not mentioned in the referred manuscript, If I can compute P(y=0|fk), Can I compute P(y>0|fk) as 1-P(y=0|fk) in this case?

If not, Can someone help in computing P(y>0|fk)?

You can find the the referred paper here https://doi.org/10.1175/MWR3441.1

Thanks

Hi,

I want to perform a climate change impact/scenario analysis using the "HSPF" hydrological model for streamflow and some others outputs. For that, I need projected/future meteorological data time-series that I can use as input in my HSPF model. I am able to download precipitation, temp, wind, etc time-series projections for my desired period (2020-20100) from CMIP5 or CMIP6 climate models. However, I am not getting the "dew point temp" and "Solar Radiation" variables from these two climate models. Let me know if I am missing anything or if there is any other smart ways to get these two variables. Highly appreciate your help.

I am going to use climate models in working climate change prediction. I want to get if there are software other than EdGCM.

I have now doubts as to whether the scientists who play a key role in the political goals or who are consulted have the real, appropriately up-to-date perspective based on two facts: On the one hand, the ocean vortex, even temporary, have a much more impact on the climate than previously believed and this has not been included in the previous climate model calculations. In spite of the high-performance computers, are the climate model calculations at all adequately good in order to pass such far-reaching laws on it, if only a few ocean eddies might in reality cause a different climate than the one previously calculated? Link to an German article on this: https://www.eskp.de/klimawandel/ozeanwirbel-die-fleissigen-helfer-der-meeresstroemung-935695/ Second, that methane is broken down in the atmosphere after approx. 8 years, but stores 25 times more heat than carbon dioxide and this again has not been adequately taken into account in the climate protection agreement. In addition, large amounts of methane are measured, the origin of which cannot yet be explained. Link to an German article on this: https://www.eskp.de/klimawandel/ozeanwirbel-die-fleissigen-helfer-der-meeresstroemung-935695/

I know there are some global database that include rainfall. But I would like something more specific for Africa at a spatial resolution around 0.25 degrees, and daily frequency for the last decade. Something equivalent to the European ECA&D would be great. Does it exist for Africa? Thanks.

I have a downscaled precipitation data, which needs to be bias-corrected. For the process, I tried the non-linear correction technique, which does one station at a time.

So is there any guiding idea, code, manual, or reference, which is done by using the SVM principle to apply for large data and many stations at a time?

In addition, I am also seeking the application of machine learning with SVM in climate data dis-aggregation into fine temporal resolution such as an hourly basis.

I have temperature and precipitation data of 30 years and I'm willing to study the trend over these years of Kathmandu city. I'm very new to this study so would be really grateful if you would suggest me the statistical test and the softwares that could be used.

I am applying interpolation by kriging method using GIS and interpolation did not cover the whole of area under consideration.

How to do this? Please guide me.

Regards

Naveed

I am working on climate modelling. It sometimes shows error in R when I go for the visualization of climate scenarios with confidence interval. I need the R code of multiple time series with confidence interval.

There are total of nine GCM from CIMP 6, and I am confused which model suits best for my study . I want to determine change in land suitibility with respect to 3 SSP, RCP 2.6, 4.5 And 8.5 for year 2060.

I would be grateful to your response.

Thank you

Hello,

I have a Cordex NetCDF file with rotated pole coordinates. I would like to transform those coordinates to regular lat/lon coordinates. I am working on CDO. But, I am also open to solutions on R or Phyton or Matlab.

I attach the "cdo sinfo" of my NetCDF file and the gdalinfo.

Thank you

I am using SDSM for statistical downscaling and keen to know that which I should prefer to use GCMs or RCMs output. Keep in mind that my study area is high elevated about 3700 m a.m.sl to 4800 m a.m.sl and 137, 000 Sq.km.

I have longterm daily temperatures and precipitaions data not uniformly located observation stations.

Please share your expertise, for each temperatures and precipitaion separately.

Regards

Naveed.

These SSPs are now being used as important inputs for the latest climate models, feeding into the Intergovernmental Panel on Climate Change (IPCC) sixth assessment report due to be published in 2020-21.

what is Shared Socio-economic Pathways(SSPs) and how to used for maxent?

I mean I want to konw the relationship of the climate and Shared Socio-economic Pathways(SSPs)

Hello to you,

I would like to know if someone here can explain to me the difference between the following models for urban microclimate analysis before I start researching each one myself:

- CitySim

- SOLENE MICROCLIMATE

- PALM-4U

- Austal2000

- Urban climate model MUKLIMO_3

- SURFEX

until now i try to modeling my area in ENVI-met and I found that the interface is very convenient to use. What are the advantages of other models over it.

thanks to the helpers

I know some papers reconstructed the paleo PDO by proxy data. But the time periods of PDO are usually thousands of years ago. I want to know something about the PDO in deep-time geology period.

Thank you for your kindly help and useful discussion!!!

I have historical and future climate data derived from different RCMs for different geographic locations. Each two groups of data are of unequal sizes. I did a Welch t-test on my samples and I sometimes find a significant difference between the two types of data and sometimes not!! Is that normal? and is it really necessary to test the significance of the data? I saw similar types of studies that did not do it.