Questions related to Climate Modeling
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).
What are the research results that support the thesis that as a result of human civilization, as a result of still increasing greenhouse gas emissions, the process of global warming in recent years has accelerated faster than previously predicted?
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:
What are the results of research supporting the thesis that, as a result of human civilization, as a result of still increasing greenhouse gas emissions, the process of global warming in recent years has accelerated faster than previously predicted?
Has the process of global warming in recent years accelerated faster than it was predicted just a few years ago?
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.
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.
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.
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?
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?
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?
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).
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?
What are the following things called ?
Are they addressed as Climate Indices or Teleconnection ?
- North Atlantic Oscillation (NAO)
- East Atlantic West Pacific Oscillation (EAWP)
- Scandinavian Pattern (SCP)
- Niño 3.4 (the region encompassing 5°N–5°S, 120°W–170°W)
- Pacific Decadal Oscillation (PDO)
- Arctic Oscillation (AO)
- Antarctic Oscillation (AnO)
- Southern Oscillation Index (SOI)
- Dipole Mode Index
What is the usefulness of the above-mentioned climatic indices? From where can we download these indices ? How these climatic indices are related to Climate Change ? If there any research article explaining the fundamentals and giving step by step methodology there on how to relate these indices with climate change ?
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?
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 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:
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?
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?
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.
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!
"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?
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.
% ---------------- *** 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?
% ---------------- *** 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.
% ---------------- *** 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.
% ---------------- *** 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.
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
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
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......I need help ...... .
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?
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 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!
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!
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?
Indian Agricultural Research Institute, New Delhi
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,.).
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?
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:
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
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 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.
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.
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.
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.
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:
- SOLENE MICROCLIMATE
- Urban climate model MUKLIMO_3
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.
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?
I am interested not in the technical description but in the case studies, e.g., when the variability of various fluxes is studied.
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?
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?
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.
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 ?