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I got two climate datasets containing variables for 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 processes like permafrost, and it's evaluation in time. I thank you in advance for your time and consideration on this matter.
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You are not the only one who doubts the existence of global warming. I noticed this after 1998, when there was a short but very statistically significant increase in temperature. It was not caused by the greenhouse effect. Look at charts from the internet.
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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?
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Climate change is a reality, but we must found a reference to be sur about the tendency of change. No one can explain which is the next step for climate change.
The statistical test can not respond to a dynamique change.
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What are the following things called ?
Are they addressed as Climate Indices or Teleconnection ?
  1. North Atlantic Oscillation (NAO)
  2. East Atlantic West Pacific Oscillation (EAWP)
  3. Scandinavian Pattern (SCP)
  4. Niño 3.4 (the region encompassing 5°N–5°S, 120°W–170°W)
  5. Pacific Decadal Oscillation (PDO)
  6. Arctic Oscillation (AO)
  7. Antarctic Oscillation (AnO)
  8. Southern Oscillation Index (SOI)
  9. 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 ?
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It's very simple. the term climate covers a time interval of several hundred years. This is true? If so, what global knowledge do we have about the climate in 1700, for example? Measurement of air temperature and pressure at different points on the Earth. discrete changes. Is it true that there were no satellite measurements? Does this mean that the modern system of weather monitoring is still insufficient for studying the climate? The study of climate using atmospheric indices corresponds to the science of 1900. I am simplifying this. what is the atmospheric circulation index? This is a time series of atmospheric pressure differences at two points, for example. If these are observation points in one climatic zone, then this is scientific. And if not? This is about atmospheric circulation indices. Now about teleconnection. Climate change has causes. For example, a volcanic eruption changes the optical characteristics of the atmosphere and cooling sets in. You can study these reasons. We can say that there are no reasons. Everything happens by chance. And then, by insignificant temperature changes in the equatorial zone between Australia and the island of Tahiti, for example, you predict climate change in India. Do you agree that changes in pressure in the equatorial zone cannot be the cause of climate change in India? This is teleconnection, when they study not the causes but the correlation.
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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?
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I agree with Toni Klemm.
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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!
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Thank you, I will try it.
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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.
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Hi Berika, I'd recommend to use all 5 as they are already the result of a selection process. But if you want to select only 3 then this document (section 6 and 7) will be useful: https://www.isimip.org/documents/413/ISIMIP3b_bias_adjustment_fact_sheet_Gnsz7CO.pdf
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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?
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R. T. Corlett Thank you for your answer!
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I am going to use climate models in working climate change prediction. I want to get if there are software other than EdGCM.
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Yes
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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?
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Dear @Sam Nicholson, I'm afraid climate models do not calibrate any parameter. These models are developed considering different physical processes in terms of their equations. The number of physical processes considered, the way in which they are analytically described and also numerically implemented, depends on different factors, including the spatio-temporal discretization of the climate model (i.e. grid dimension, time step length) and the total temporal horizon to be simulated. The evolution of the state variables of climate models depends on different model forcings such as the income radiative flux affecting the modeled system through the physical processes considered by the model (e.g. vapor condensation, evaporation, moisture recycling, etc. ). Therefore, to simulate the expected paleoclimate trends, which are often revealed by different proxies, you should include the corresponding climate forcings in the model, which are supposed to generate such paleoclimate trends.
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Hi all,
Besides CESM LENS2, which modeling groups provide large ensemble experiments in the CMIP6 era?
Thanks
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Dear Oliver
Why don't you use the ESGF database as your main source?
If you do not know how to use it for your purpose, I can help you.
Last year I started my research by choosing some SSP scenarios from this website.
Here is my first article:
Best regards
Amir
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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?
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Dear Sam, I'm afraid climate models do not calibrate any parameter. These models are developed considering different physical processes in terms of their equations. The number of physical processes considered, the way in which they are analytically described and numerically implemented depends on different factors, including the spatio-temporal discretization of the climate model (i.e. grid dimension, time step length) ensuring the numerical convergence of the solution at every time step, and the total temporal horizon to be simulated. The evolution of the state variables of climate models depends on the different model forcings such as the income radiative flux affecting the modeled system through the physical processes considered by the model (e.g. vapor condensation, evaporation, moisture recycling, etc. ). Therefore, to simulate the expected paleoclimate trends, which are often revealed by different proxies, you should include the corresponding climate forcings in the model, which are supposed to generate such paleoclimate trends.
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I have seen in some discussion forums that climate projection models do not reliably reproduce twister phenomena. How can climate modeling overcome this challenge ?
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You have outlined an alarming new phenomenon that everyone hopes it does not happen near them, thus avoiding any discussion about it.
However, you have noticed that it is happening everywhere on the planet today, while previously it mostly occurred along the "twister corridor" in Central USA.
It has spread far and wide, also increasing its intensity and killing many more people than it did before.
Meteorologists in the USA have been able to predict successfully and give warnings, but sometimes hurricanes take place at night and you can't see it coming.
Lots of research can be done on this subject: When can we expect one, how is it generated and what conditions lead to the formation of one. All these are incompletely known today.
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I am looking for a Matlab toolbox/function for the visualization of climate projection to get the similar figure in the attachment.
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The shadow range represents the uncertainty between models, and you can use quartiles to define upper and lower limits, for example:
Slow=quantile(ysample,0.125); Shigh=quantile(ysample,0.875);
h1=patch([yr' yr(end:-1:1)'],[Shigh Slow(end:-1:1)],[0.8 0.8 0.8],'linestyle','none');
Ilow=quantile(ys,0.125); Ihigh=quantile(ys,0.875);
h3=patch([yr' yr(end:-1:1)'],[Ihigh Ilow(end:-1:1)],[1 0.7 0.7],'linestyle','none','facealpha',0.8);
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Grubbs's test and Dixon's test are widely applied in the field of Hydrology to detect outliers, but the drawback of these statistical tests is that it needs the dataset to be approximately normally distributed? I have rainfall data for 113 years and the dataset is non-normally distributed. What are the statistical tests for finding outliers in non-normally distributed datasets & what values should we replace in the place of Outliers?
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Hello Kabbilawsh,
If you believed your sample data accurately represented the target population, you could: (a) run a simulation study of random samples from such a population; and (b) identify exact thresholds for cases (either individual data points or sample means or medians, depending on which better fit your research situation) at whatever desired level of Type I risk you were willing to apply.
If you don't believe your sample data accurately represent the target population, you could invoke whatever distribution you believe to be plausible for the population, then proceed as above.
On the other hand, you could always construct a Chebychev confidence interval for the mean at whatever confidence level you desired, though this would then identify thresholds beyond which no more than 100 - CI% of sample means would be expected to fall, no matter what the shape of the distribution. This, of course, would apply only to samples of 2 or more cases, not to individual scores.
Good luck with your work.
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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
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Yes it is possible, this article will surely be helpful
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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.
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There is no perfect model, suitable for all purposes. This is why a wide range of climate models exists, forming what is called the spectrum or the hierarchy of models.
Citation from the source/link.
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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!
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Please, Chenghai Wang is your response a follow-up of Collins Kwame Oduro response or WRF provides another solution to my puzzle? If not, what is WRF and does this model provides resolution lower than 30km, my data has a resolution of less than 5km and want to perform future projections for the Congo Basin region. Thank you very much.
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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
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You can easily use CDO in window 10 by installing Ubuntu's terminal as a window application in window 10. Kindly follow the six steps below:-
step1: Open your window 10 and go to search are and then search the word turn window features on or off as illustrated in image1 (step1) attached below.
step2: from the popup window resulting due to step1, Scroll down until you see Window Subsystem for Linux and check it (tick) then click ok and restart your computer.
AIM OF STEPS 1 & 2: Is to create an environment for using Ubuntu (Linux) in Windows 10.
Step3: After finishing to restart your computer then search for Microsoft store and then click the app store as illustrated in image 3 (step3)
step4: After opening the Microsoft store then search Ubuntu and then install the latest version as illustrated in image 4 (step4).
step5: Open the Ubuntu terminal (App) and then create your username and password and finally update the ubuntu packages by using the command sudo apt-get update as shown in image 5 (step5).
step6: then install CDO by using the command sudo apt-get install cdo
lastly:
If you will get this error after installing CDO
# error
cdo: error while loading shared libraries: libQt5Core.so.5: cannot open shared object file: No such file or directory
THEN USE THE COMMAND BELOW
sudo strip --remove-section=.note.ABI-tag /usr/lib/x86_64-linux-gnu/libQt5Core.so.5
All the best, if you will get any challenges, feel free to share them here
PLEASE SEE THE ATTACHED IMAGES BELOW FOR GUIDANCE
Thank you
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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)
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I am wondering what have you learned with your participation with the Hellenic Centre of Marine Research, if you are still there.
There are lively discussions at RG and some people are unaware about the importance of limiting the amount of CO2 in the air.
I contributed in the education of younger generations (in Canada) who are very enthusiastic about supporting the elimination of the fossil fuels.
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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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Yes. Please see the following useful link for insights.
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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
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Dear Hasan,
this means that the weather/climate data set you're working with offers a maximum spatial resolution of 1 square kilometer, i.e., a 1 km by 1 km grid cell. So your data points will each pertain to one such 1 km × 1 km grid cell. Consequentially, you won't be able to say anything about spatial temperature variations WITHIN any single grid cell. Thus, you can only compare whole (1 km x 1 km) cells with each other. In short: Your data set only offers ONE single ("aggregate") figure for a given atmospheric parameter for a whole 1 square kilometer grid cell.
Hope this helps a bit and please do not hesitate to get back should you have any further questions.
Best,
Julius
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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.
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This is a good question.
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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
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You need first to see the supported data types in Doc/README.NAMELIST for both SST and ICBC. Then you can choose the one you want
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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?
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Depends. Are these different regions in the same climatic zone? Then the proxies would depend on your archive (mag sus, pollen , grain size, etc.) and might be correlatable. But of course, preferably you should use the same proxies in both regions if thats possible with the archives. If however, these regions are in different climate or vegetation zones or if the archives differ a lot, then the range of your proxy might be exceeded. In any case, I would recommend a robust independent dating framework. The benefit of this would be, that you might be able to chose the most suitable proxy for each site/archive/region and correlate it based on the ages. Please be more specific about the regions and available proxies and I might be able to be of some help.
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Dear Researchers,
We are aware that a shift in monsoon peak discharge may have an adverse impact on several water-based applications such as agriculture, dam operations, etc. E.g. I am interested to know how to quantify the same based on modeling approaches. Thank you!
Sincerely,
Aman Srivastava
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I think these papers could help you;
  1. On the Suitability of GCM Runoff Fields for River Discharge Modeling: A Case Study Using Model Output from HadGEM2 and ECHAM5, February 2012,
  2. Development of a high resolution runoff routing model, calibration and application to assess runoff from the LMD GCM, September 2003,
  3. Climate change and its impacts on river discharge in two climate regions in China, November 2015,
  4. Modelling the potential impacts of climate change on hydrology and water resources in the Indrawati River Basin, Nepal, February 2016,
They're available on the ResearchGate Server for download.
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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.
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I think mike she will be best suited for integrated hydro metrological and water quality modelling purpose
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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!
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I think these papers could help you;
  1. Properties of El Niño Southern Oscillation in Different Equilibrium Climates with HadCM3, October 2006,
  2. El Niño–Southern Oscillation Evolution Modulated by Atlantic Forcing, July 2020,
  3. Influence of the Coupling South Atlantic Convergence Zone-El Niño-Southern Oscillation (SACZ-ENSO) on the Projected Precipitation Changes over the Central Andes, May 2021,
They're available on the ResearchGate Server for download.
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Dear Researchers,
As it is evident that tropical climate has more variability than sub-tropical climate. Whether this causes difficulties while developing hydrological, climatological, or meteorological models for tropical regions? OR Can we attribute these phenomena as the reason for better models being developed in sub-tropical regions? Kindly comment, please. Thank you!
Sincerely,
Aman Srivastava
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Hi Dr Aman Srivastava . Yes I think so.
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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
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There is no need to use a very high specification to run WRF-ARW. Actually this depends on the resolution, region and time of forecasting. You need to install any suitable version of Linux such as Fedora, Obinto, etc. Then you should download, unpack, compile the source code that is available on the internet.
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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,
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Interesante pregunta, sería muy bueno poder acceder a sitios o software q aportaran esta información
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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
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Hi Hamideh,
The ensemble projections of eight CMIP5 climate models in Southeast Queensland, Australia are discussed in the following paper:
You will see how we justify our climate models selection for investigating climate change impacts on a catchment hydrological behaviour.
Cheers,
Mahsa
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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
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The research described in the question is outlined in a compressive manner in the NASA project
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Discussion of the state of art on the application of the Ertel's potential vorticity theorem in atmospheric physics & physical oceanography.
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.
Several links to check on the topic powered by ResearchGate:
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A very complete review on Ertel potential vorticity theorem, thank you for suggesting its reading, Prof. Aref Wazwaz .
Best Regards.
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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
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Yes. You can proceed with that formula as you deal with Precipitation data, which contains only non-negative values. according to axioms of probability P(y≠0|fk)=1-P(y=0|fk).
You can find a worked example in the book titled “Statistical Methods in Hydrology and Hydroclimatology(DOI: 10.1007/978-981-10-8779-0)”
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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.
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Just to be sure about the solar radiation question, you should address it directly to those who have published about it, e.g. Martin Wild
doi: 10.1063/1.4975554
DOI: 10.1007/s00382-016-3471-2
doi:10.1002/ 2014JD022572
doi:10.1002/jgrd.50426
DOI: 10.14171/j.2096-5117.gei.2018.04.005
DOI: 10.1175/JAMC-D-18-0013.1
doi:10.1007/s00382-016-3471-2
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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/
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Dear Eva,
As you know, in science we are continuously developing our knowledge and testing hypotheses. So, it should not be surprising to find over and over again some changes in our view on the functioning of the Earth system. In regard to modeling, famous prof Box used to say "All models are wrong, but some are useful". Indeed, models are not expected to provide a 1:1 representation of the Earth system, they must be to some extent simplified.
However, the climate models are still one of the best tools we have in science. Even if some issues were not addressed exactly in the previous versions, it does not mean that the models are wrong, as some of the effects of these processes were already included by so-called parametrization.
The key question is how the mentioned problems are important from a quantitative point of view. From my assessment, it is likely that they do not alter the general trends.
Sorry for the short answer but the issue is really extensive.
with best wishes
WItold
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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.
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Thanks to All for all these very interesting sites to get data, specially recent data and/or also different time scales. I just want to add one specificity of the Hydrosciences SIEREM monthly gridded dataset, which you can not have in any other bases: interpolation is ONLY based on OBSERVED data for each time step. We do not use any reference average with anomalies, no reconstruction, only observed data. This makes the difference with all other products, i.e. in a forthcoming paper we show that our product gives a much better correlation with observed groundbased data than all other products we compared. It is interesting specially in rainfall-runoff modelling, where we search to reduce the uncertainty of all entries in the model. Have a look here. http://www.hydrosciences.org/spip.php?article1387
and the related paper
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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.
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SVR and MRF are the best model
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Dear Researchers,
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
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Most of the comments are wrong or incomplete. First of all you need to distinguish between a particular software implementation and the underlying theory. The theory of kriging, i.e. geostatistics is not based on GIS. Nor is an software package implementing kriging limited to or necessarily related to GIS so the original question implies a serious mis-understanding about kriging and GIS
One of the basic assumptions underlying kriging is either second order stationarity or intrinsic stationarity. Unfortunately with only a finite data set it is not possible to test whether either of these assumptions is really valid. There are ways to ascertain whether it is "reasonable" to make one of these assumptions or rather whether there is evidence that both assumptions are unreasonable. e.g if the empirical variogram increases at a quadratic or higher order then intrinsic stationarity is not reasonable.
If having modeled a variogram with a sill and hence at least a practical range then you would want to be cautious about using kriging at a distance greater than the range from all the data locations. Two things will happen; the kriging variance will be much larger and secondly the "kriged" value will be essentially just the arithmetic mean of the data values. A particular software implementation of kriging might then prevent generating kriged values too far away from all data locations.
Software manuals, particularly those for commercial software are likely not sufficient to address questions such as these. Instead one should consult a book on geostatistics, e.g the one by J.- P. Chiles and P. Delfiner (John Wiley & sons)
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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.
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Hello Shihab,
This is a late answer, but here you can find a good explanation of how to calculate by hand "Prediction intervals" (that is the correct name, instead of confidence intervals) for time series depending on coverage probability that you are looking for.
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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
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Hi Arjun,
There is no right or wrong GCM perditions as long as you are forecasting the future. Different GCMs will predict different results and this model uncertainty need to be addressed and/or reduced in future forecasting.
V. Daksiya
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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
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Catriona Duffy I used a different method. However, it seems to work for Mostafa, so try it!
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Dear Researchers,
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.
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SDSM is better for monthly tem and precipitation. GCMs may be perfect for daily data prediction.
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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)
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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
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I prefer ENVI-MET model for microclimate analysis. Because it has given more accuracy with Greater resolution building to building temperature in compare to
CitySim, Urban climate model MUKLIMO_3, SOLENE MICROCLIMATE.
Please go through this articles given below.
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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
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Decadal bioclimatic variables may also be prone to the uncertainty related to the selection of the specific month/quarter (e.g. wettest quarter). This paper might be relevant for you:
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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!!!
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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.
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Using the significance test is not always necessary for all researches. Qualitative researches, for example, does not need a significance test. As for the quantitative researches, it depends on the nature of the research.
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I am running climate models and need help on calculating monthly averages of daily streamflow date from multiple model outputs, thanks for your help!
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Thank you everyone for your insights!
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I want to know about EICM... What is the data inputs and outputs...........What methodology can be used to define a good EICM...Can this model be used for the prediction of temperature of pavement.
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Any idea: Where can I get the latest version of EICM?
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I am trying to understand how future river flows can be predicted accurately from various climate models and/or variables. I have found various datasets for water discharge into sea, can analysing the change in water discharge into the sea give you an accurate measure of the overall changes in the water flow in the river?
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you con see this paper, could be help you to understanding the climate change modelling.
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I am interested not in the technical description but in the case studies, e.g., when the variability of various fluxes is studied.
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Hi I use a free webpage called Publication Hub: https://cmip-publications.llnl.gov/
You can filter by variables name or experiments (Historical, pre indutrial, RCP2.6, RCP4.5 , RCP6.0 and RCP8.5) .
I hope it helps.
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I know, generally, climate models cover the atmosphere, oceans, land and ice-covered regions of the planet. But does the model consider the change in land-use of a particular region when estimating the projections for 100 years?
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Most models use fixed land surface characteristics for climate projections. Because, the input data required for land use transitions involves a complex interplay of many factors. For example, a land use or cover transition from bareground or natural forest to agricultural or urban requires huge estimations to be used for projections. Such estimations also impart huge uncertainty to the outputs of climate models. Thence, most prefer to fix the land use/ cover type fixed to the current scenario. Hopefully, with increased observation information and improved computational resources, future climate models may consider implementing these land use transitions in climate model simulations. For more information, please read https://www.sciencedirect.com/science/article/abs/pii/S2212095517300858.
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In ungauged watershed, I have a calculated peak hydrograph by a climate model.
I generated a GIS-based Unit Hydrograph.
I don't have a Hyetograph that might cause this peak to happen.
How to create a Direct Runoff Hydrograph reaching this peak based solely on this UH and the given peak hydrograph?
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intricacy of unit hydrograph is the effective and efficient method to quantify direct runoff hydrograph with a precipitation per unit time series.
Application of remote sensing is necessary
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How can I know the elevation of a grid point from ERA5-LAND product? With ERA5 I can obtain this parameter through the geopotential height, however, in ERA5-LAND I can't find the altitude of a grid point.
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Thank you Samuel. I've already registered, and I was trying to obtain the orograpy for ERA5-Land (0.1 resolution) which is not the same of ERA5. The C3S support gave me the response.
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I want to do an experiment by fixing individual GHG's one by one. How can I do this in RegCM4.6? I found an option under ICBC Global data input control but it is fixing all the GHG's at the same time. Please help.
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@ Praveen, just have a look into the attached file.
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Hi All,
I have calibrated my SWAT Project using SWAT-CUP successfully. I now want to incorporate baseline and future climate data into calibrated SWAT Model. As the SWAT CUP gives the best parameter ranges after successful calibration how should I incorporate these ranges in Arch SWAT Project?
If I use SWAT-CUP and copy the tmp and pcp files in the calibrated projected folder what should be the format of pcp and tmp files as they are inserted individually in ArcSWAT for each station but collectively found in pcp and tmp files of SWAT-CUP?
Which out of these two methods s accurate and more reliable?
Also when i use the fitted best parameters into my uncalibrated ArcSWAT folder and run with the same data the results are quite different from those in SWAT-CUP calibrated model , why the results are different in this case ?
TIA
Regards.
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There are some ways, 1) you should generate weather datasets for all observed stations and then add them into ArcSWAT and copy the PCP1.pcp and TMP1.tmp files and run calibrated text files with new pcp and tmp files.
The most important thing here is how to generate precipitation datasets. You should work with multi-station software such as CMHYD or MRD, etc.
2) Use weather adjustment options in SWAT.
Do not use single-site software such as LARS-WG. Because the SWAT model is (semi-) distributed model and could use one to several weather stations. Also, the location of the stations is important.
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When we download different hydroclimatic data from CORDEX, we can observe that most of the data are with a resolution of 0.44 degree latitude × 0.44 degree longitude. Is there any particular reason for such resolution?
Few RCMs provide data with 0.22 degree latitude × 0.22 degree longitude as well, but only for Europe region. However, the question remains the same. What is the necessity of such 'odd' resolution?
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Wow!! That's again a fantastic piece of suggestion. I was roughly going through your suggested book from google book. It's really helpful. Thanks again for this valuable suggestion, Sir!! Janusz Pudykiewicz
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Albedo plays a central role in the climate change energy budgeting, but its generic use (from the astronomy tradition) creates problems of many kinds.
Firstly, even the classic astronomic use appears defective. For example, a very reflective space object would appear dark in color because it reflects the darkness of space. Thus that object would be interpreted as being warmer than a white object (because it appears to absorb more solar radiation than the white object); this is not true because the very reflective object absorbs less radiation than the white object.
In
Deleted research item The research item mentioned here has been deleted
we have shown a few such deficiencies of albedo and have solved those problems. Albedo is just a too large approximation!
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Yes Stamatios, I fully agree and I do not understand why the IPCC climatologists say that since the albedo of forests is 5% larger than that of bare ground, cutting down the forests produce cooling of the Earth. In my publication I show that in fact deforestation has a strong warming effect, not cooling. The IPCC position is regrettable at this time when there is so much deforestation...
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How spatial inhomogeneity in paleoclimate records biased the interpretation of past climate variability. In some of the model-based studies, the advantage of spatial homogenous gridded data has been discussed beautifully. But I couldn't find any literature or article dealing with the role of the spatial distribution of climate data in paleoclimate study.
I would appreciate your suggestions.
Thank you
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The inhomogeneity of paleoclimate geography was common in deep time. Similar with the Quaternary and present time, inhomogenetic paleoclimate zonations can be interpreted to the results from landform alternation (monsoon, rain shadow), high-pressure zone (Hadley circulation) shifting, and so on. Some cases of the the Cretaceous climate can be found from literatures. Hope it is helpful for your question.
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I downloaded Cordex historical data from Copernicus CDS (https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-single-levels?tab=form). Data are in NetCDF format. When I read the files using a NetCDF reader, I see that they have a Lambert Conformal Conic projection type (screenshot attached). I need to extract weather data for specific locations using regular coordinates. Could someone please tell me how to deal with these NetCDF files so I can be able to extract my data?
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Thank you Mr. Reuben Reyes
But to use GDAL you need to install Phyton, right?
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When running the WRF climate model, output for a single day is generally from 00Z to 00Z (6 or 7PM) depending on CST or CDT. The input data for the simulation for a single day is also from 00Z on the first day to 00Z on the next day.
I am using some of this output (daily max/minimum temperature, daily precipitation, and daily solar insolation) to drive a crop model. Before I run the crop model, I would like to see if it is possible to get daily output from my WRF model simulation to go from midnight (06Z) to midnight (06Z) instead. The reason for this is because it will give a more reasonable daily maximum/minimum temperature, precipitation, radiation values that can then be used as input into the crop model.
My question...how do I rearrange the time of my WRF simulation, so that my climate model output for a single day is valid from midnight to midnight? Any help would be greatly appreciated. Thanks!
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As far as I know, WRF is configured to work without thinking about offsets from 0Z. Otherwise it would complicate the use of the lookup tables and sub-daily parameters in the models (and parameterizations). My recommendation would be to handle the time conversion in your post-processing. The specifics would depend on which particular set of tools you use.
For example, if using the pandas library in python, you could use the DateOffset object to shift your time coordinate by the appropriate amount, then use groupby (by day) to get your daily statistics. On xarray, another popular tool in python, you can use the shift function to move move your data along your time coordinate, then use groupby to get your daily statistics.
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I'm using CORDEX climate model data (over the US) for the first time and the rainfall is supposed to be in kg/m2/s according to the documentation. However, when I convert this I get crazy values, often many metres of rain in a day. I've seen a comment (referring to Australasian data but presumably also true elsewhere) saying it's out by a factor of 24, but even dividing by 24 leaves me with very large values. Does anyone know what the units are?
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Dear Clare
You can use the FTP dataset.
For example, this website is to Asia (http://cccr.tropmet.res.in/home/clim_paleo.jsp ).
Good luck
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I'm looking for downscaled climate projections for various regions, but so far have only been able to find these projections for US. There is abundance of data on climate models from sites like Cera, but none of them are downscaled projections. Any help would be immensely appreciated.
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Have a look at the Coordinated Regional Climate Downscaling Experiment (CORDEX) for different regions!
Cheers!
Vaid, B. H.
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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.
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Thanks
Ali Alwaeli
for the info.
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Hello everyone!!
i'm predicting the future rainfall using SDSM Software.
while calibrating (Period 1970 to 2000) the model for, conditional monthly frequency, SDSM is giving an error says " An error has occurred- please check all the selections. overflow has occurred ".
checked for the Predictand length it looks perfect for 366 days a year setting.
Predictors are Canadian Based NCEP-NCAR (1961 to 2005) of the box (27x , 41y) seems correct, all the variables appears while calibrating.
not able to Debug please do help.
check the Attachment for the error message.
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Hello Sir, I am also facing this problem. can You please suggest me the solution.
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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?
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Dear Evan,
I think that a two GCMs and two scenarios/RCPs (2×2 = 4 combinations) is the minimum if you want to display the uncertainties related to both the GCMs and scenarios. But if you work in R or any other environment/software that can automate the process, several RCMs/scenarios might be easily processed.
In respect of the trade-off between the number of GCMs and the number of scenarios, I suggest using more scenarios than GCMs, since readers are rarely familiar with the specific features of the selected GCMs but can interpret easily the differences between the 4 RCPs.
HTH,
Ákos
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Hi,
I downloaded grib2 data for Temp at 2m, U at 10m, v at 10m for the same period. Now how can I convert these three files as a single grib2 file?.
Thanks & Regards,
Malleswararao
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For grib files;
cat infile1.grb infile2.grb > outfile.grb
For netcdf files;
to merge along the time axis;
cdo merge infile1991.nc infile1992.nc outfile1991_1992.nc
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The rapid evolution of the El Nino-La Nina cycles should be a cause of concern.
Catastrophic, cataclysmic climate change may be much closer than we all think.
Altering of global ocean currents is a major concern, as it drives the Earth's weather patterns.
Changes in the Jet stream and the recent aberrant movement of the polar vortex may portend this.
If these currents are already shifting, then we are late in our action.
Waiting for a temperature rise may not be a wise strategy
International plans to change global warming over 10 or 20 years may be too slow an undertaking.
Do we need to act in a radical way today?
Do we wait for our Leaders to 'Lead'?
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Thank you Fazleh Mahomed, for sharing this important and vital topic. Our environment suffers from the great pollution surrounding it. We are responsible for protecting and preserving our environment as much as possible. Even if it's late.
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Hi,
I would like to calculate the hydrological budget (HB) of the region which is partially covered by forest and heavily influenced by human activity. As I understand, the hydrological budget is largely dependent on the following factors (i) moisture contribution (regional and local), (ii) evaporation from the local water bodies, (iii) and evapotranspiration from the forest.
Is there any way by which we can calculate the hydrological budget from any region using the factors I have discussed above.
Also, it would be nice, if someone can suggest any papers to calculate the HB of the region.
Thank you
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