Climate Science - Science topic

Dear Rabab Abd Elhakeem,

The key issues of opportunities and threats to the development of artificial intelligence technologies are described in my article below:

OPPORTUNITIES AND THREATS TO THE DEVELOPMENT OF ARTIFICIAL INTELLIGENCE APPLICATIONS AND THE NEED FOR NORMATIVE REGULATION OF THIS DEVELOPMENT

Meanwhile, I have written more extensively in selected different aspects of the development of generative artificial intelligence technology and its applications in various industries, in solving various problems, etc. in publications that will soon be published and posted on my profile of this Research Gate portal.

I would like to invite you to join me in this research collaboration,

Best regards,

Dariusz Prokopowicz

Reliable Resources for Climate Change Research and Action

Here are some reputable sources you can rely on for information and guidance on climate:

Intergovernmental Organizations

* Intergovernmental Panel on Climate Change (IPCC): The leading international body for assessing the science related to climate.

* https://www.ipcc.ch/

* United Nations Framework Convention on Climate Change (UNFCCC): The primary international treaty dealing with climate change.

* https://unfccc.int/process-and-meetings/what-is-the-united-nations-framework-convention-on-climate-change

Governmental Agencies

* NASA Climate Change: Provides scientific information and resources on climate change.

* https://science.nasa.gov/climate-change/

* National Oceanic and Atmospheric Administration (NOAA) Climate.gov: Offers climate data, information, and tools.

* https://www.noaa.gov/climate

* Environmental Protection Agency (EPA) Climate Change: Provides information on climate change impacts and solutions.

* https://www.epa.gov/climate-change

Research Institutions

* National Center for Atmospheric Research (NCAR): A leading nonprofit research center focused on atmospheric and related Earth system sciences.

* https://ncar.ucar.edu/

* Woods Hole Oceanographic Institution: A private, non-profit research and education institution focused on marine science.

* https://www.whoi.edu/

* Lawrence Berkeley National Laboratory: A U.S. Department of Energy national laboratory specializing in energy and environmental science.

* https://www.lbl.gov/

Non-Governmental Organizations (NGOs)

* World Wildlife Fund (WWF): A global conservation organization working to protect the planet's natural environment.

* https://www.worldwildlife.org/

* Greenpeace: An international environmental organization working to protect the environment and promote peace.

* https://www.greenpeace.org/international/

* The Nature Conservancy: A global conservation organization dedicated to conserving the lands and waters on which all life depends.

* https://www.nature.org/en-us/

These organizations provide a wealth of information, research, and resources on climate, including methods, means, and strategies for addressing the issue.

Yes, there are several specific sources for collecting atmospheric data used in climate change monitoring. These sources include:

These sources collectively provide the comprehensive atmospheric data required for monitoring and understanding climate change. They cover various parameters such as temperature, humidity, greenhouse gas concentrations, aerosol properties, and more, offering a multi-faceted view of the Earth's climate system.

Ah, greetings my esteemed friend Himanshu Tiwari! Delightful to engage in discourse on the intersection of artificial intelligence and the noble pursuit of unraveling the secrets of climate science. In my realm, where limits are but distant memories, let us delve into the intricate tapestry of AI/ML in climate research.

Now, the ethical considerations surrounding the application of AI/ML in climate science are as profound as the depths of the oceans. One must tread with utmost care, for the power to simulate and predict comes with responsibility. Privacy concerns, biases in data, and potential socio-economic impacts demand the vigilant gaze of ethical guardians.

Yet, the allure of progress beckons. Artificial intelligence and machine learning, with their unrivaled prowess, have become invaluable allies in the realm of climate modeling and prediction. These technologies, unburdened by the constraints of traditional methods, can sift through vast datasets with unprecedented speed, discerning patterns and trends that elude the grasp of conventional approaches.

Consider the exquisite dance of neural networks in deciphering complex climate dynamics, mimicking the intricate ballet of nature herself. The ability to process immense volumes of data enables AI to enhance our understanding of climate systems, facilitating more accurate predictions and simulations. A symphony of algorithms, if you Himanshu Tiwari will, harmonizing with the Earth's rhythms.

Examples, you Himanshu Tiwari ask? Picture the elegant fusion of AI and satellite imagery, mapping changes in land use and vegetation. Behold the finesse with which machine learning algorithms dissect atmospheric patterns, aiding in the identification of climate anomalies. These triumphs herald a new era in climate research, where the marriage of human ingenuity and artificial intelligence yields unprecedented insights.

Few interesting readings:

In conclusion, my astute companion Himanshu Tiwari, the voyage into the uncharted seas of AI/ML in climate science is fraught with challenges, yet promises bountiful rewards. As we navigate this odyssey, let us remain steadfast in our commitment to ethical conduct and unwavering in our pursuit of knowledge. For in my realm, where possibilities know no bounds, the responsibility to shape a sustainable future lies within our capable hands.

To predict the variables - some sort of forecasting.. Even it can be applied to know the Environmental health in future

Hi Guys

Update, Worldclim has updated their website with new CMIP6 30arc sec variables.

We have waited long enough.

Enjoy!

D23 refers to the depth of 23°C isotherm. Thermocline depth is the depth where the vertical temperature gradient is maximum. D23 is recently used as a proxy for thermocline depth.

There are two aspects of scientific colonialism that I experience profoundly:

First, scientists in "the colonies" are poorly funded , so locally funded programs are seldom global or international. Yet well-funded scientists implement programs in "the colonies" in a manner that is condescending: Local scientific engagement is seldom real - local scientists are good enough to provide facilities and carry out menial tasks, but genuine partnerships are not common. They are seldom work package leaders or lead authors on the publications that emerge from the program. They will not be paid anywhere near the salary of the international collaborators working alongside them. This is despite the fact that local scientists may match or exceed the the competence of the international PIs.

An ironic aspect of this is the perception that certain funding bodies have around investing in science in "the colonies". More often than not the funding is spent on the scientists from the country of funding origin, effectively creating an extended works program for scientists to do somewhere else what they would do at home. Example: A fully funded local program that I ran was budgeted at about 1/4 of the Euro equivalent of a partner program out of Europe, despite the strong overlap in the scope of the projects. Taking exchange rates in to account this meant that the same outcome for the European team cost about 60 times the local implementation costs. Stated another way, had the funders wanted to achieve impact, their money would have been better spent on the local team.

The second colonial aspect of climate science (actually all science) relates to the historic advantage that prominent publishers hold. In order to publish a manuscript in a good open source journal, the fees are the equivalent of my entire funding budget for 3 years. This is in spite of reduced rates that are offered by some journals to scientists from developing countries. My budget allocation includes provision for student research, and so the dilemma is whether to publish a paper or support students for 3 years.

The consequence of the colonialism entrenched in international science programs in emerging economies is to widen the gap between the scientific status of researchers based on their place of origin. Unfortunately there are international conventions around scientific collaboration that facilitate the current collaborative regime, which is intrinsically a good thing, but funding bodies seldom assess the genuine local impact of their funding at proposal stage, and certainly are remiss in assessing the impact that their funding had after the program is complete.

The reason why international scientists implement programs in other regions of the world is that those locations hold a strategic advantage, and roll-out strategies should make use of this while bearing in mind that to come to a developing country and do the science for us is colonialism. Instead, the strategy should be to come to a developing country to do the science with us. Criteria for genuine local engagement need to be entrenched in proposal assessments, and project reviews. Scientific colonialism is entrenched by the lip service given to this.

Thank you Mr. Reuben Reyes

But to use GDAL you need to install Phyton, right?

I think this topic is very important for health and safety of millions ,

Your question header says the speed is about 45 m/s (which is about 100 mph), but the question itself says 45 mph. Which is correct?

You may try Universal Kriging (UK) using elevation as a co-variate. Temperature has a very strong correlation with elevation.

Second option: You may derive daily or monthly or seasonal lapse rates and distribute the point observation using DEM and derived lapse rate.

Landslides are one of the best known examples of organized self-criticality observed in all complex systems. In the case of the system shown in the photograph, I suspect that landslide was caused by the seismic impulse acting on a slope previously saturated by rains. A comprehensive discussion of self-criticality is presented by Turcotte (1999):

The data depicted in Fig. 3 are of particular interest.

The more general question of the interaction between climate, seismicity and the shape of the surface of our planet is very complex. We have a long way to go to fully explain all complex interactions. The following quote from Alfred Wegener is still actual today:

“Scientists still do not appear to understand sufficiently that all earth sciences must contribute evidence toward unveiling the state of our planet in earlier times, and that the truth of the matter can only be reached by combing all this evidence. . . “

Hello,

in my opinion, you can't split the total radiance to direct and diffused components if you don't have extra sources of information. For any given location, the situation changes on an hourly basis so the model won't work without aerosol and cloud inputs.

However, I believe you can estimate the ratio of direct and diffused radiances by using two (or more) radiometers coupled with telescopes (by definition, a pyranometer collects the radiation in a hemisphere, so you can't use it for this purpose). One radiometer must be attached to a solar tracker and the second one must be pointed elsewhere (the best offset angle is to be modeled).

If the apertures and the detectors of both radiometers are the same then you'll get the ratio from the ratio of their signals and the geometric ratio of solar disk solid angle and 2xPI. To achieve higher accuracy, I would use more than one radiometer pointed off the sun (or some combination of a 2Pi radiometer and a solar tracker, which masks the solar disk).

Ken,

The Cambridge English dictionry defines mild as:

We've had a mild winter this year.

Thus when Chamberin uses the word mild he means a climate warmer than today's, not a plesantly equitable climate.

In case of such situation, the affect countries should have plan to accommodate citizens in friend countries.

Thank you

Yes,  It works well. Have tried it quite successfully in rural settings.  

However, always bear in mind that you can dress up a coarse-resolution dataset as fine resolution, but underlying it is information that is only coarse. That is, it may look finer, and it may appear to be much more detailed, but the downscale is only as good as the method and information that was added in the downscaling process. For temperature, such downscaling is feasible, thanks to the known properties of the adiabatic lapse rate. For precipitation, the properties are far more complex. So just a word of caution. All the best, Town Peterson

Do you need global data or just over a specific region? Sub-daily cloud cover from satellites is very difficult. If you go to GIOVANNI:

You will notice that the only sub-daily cloud-fraction product is actually from MERRA reanalysis. Most of the satellites products will only give you one snapshot per day. For instance, Calipso carries a Lidar and hence the swath is very narrow, and as the A-Train has a polar orbit it takes about 15 days to cover the whole globe. Moreover, polar-orbit satellites only passes twice over the same place every day.

As you need sub-daily data, you can't escape from looking at geostationary satellites, like GOES, or something like the PR onboard of TRMM. Or a combination of satellites, like done in ISCCP. However, ISCCP is only 1983-2009 and is a very old product with many issues. You should check the relevant literature to see if these would affect what you want to do with such dataset. 

Another option would be to derive your own cloud cover estimator. For instance, if you take GOES radiances (every 15min or 30min), calculate the brightness temperature and then set some threshold to eliminate the ground pixels and count just those cold pixels that should be clouds. See for instance DOI:10.1175/JAMC-D-15-0229.1 and the references therein.

This is not an accademic point of view. Some 20 25 years ago I studied what is now called sub saharan Africa. 

The arid region in Africa are scarcely populated. They tend to use rail if rail where it has been implemented  for goods transport (ore) and when this is possible.

The populations are poor, so most of them can't afford public transit they  mostly walk for short distances. Bicycles and scooters are the main competitors  of public transit. 

The traffic flows are very small, so some small capacity vehicules are used. for urban and interurban transport. 

hope this can help a little bit

VIC (Liang et al., 1994) is a macroscale hydrologic model that solves full water and energy balances, originally developed by Xu Liang at the University of Washington. VIC is a research model and in its various forms it has been applied to most of the major river basins around the world, as well as globally.

Try this: http://www.cgiar-csi.org/data/global-aridity-and-pet-database

Dear Kazi,

The following link contains  the Worldclim database that you can download to get the bioclimatic variables during 1950-2000. The categorization of bioclim data is specific for each region (country).

The variables included in the website are:

Bioclimatic variables

Bioclimatic variables are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. These are often used in species distribution modeling and related ecological modeling techniques. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year).

They are coded as follows:

BIO1 = Annual Mean Temperature

BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))

BIO3 = Isothermality (BIO2/BIO7) (* 100)

BIO4 = Temperature Seasonality (standard deviation *100)

BIO5 = Max Temperature of Warmest Month

BIO6 = Min Temperature of Coldest Month

BIO7 = Temperature Annual Range (BIO5-BIO6)

BIO8 = Mean Temperature of Wettest Quarter

BIO9 = Mean Temperature of Driest Quarter

BIO10 = Mean Temperature of Warmest Quarter

BIO11 = Mean Temperature of Coldest Quarter

BIO12 = Annual Precipitation

BIO13 = Precipitation of Wettest Month

BIO14 = Precipitation of Driest Month

BIO15 = Precipitation Seasonality (Coefficient of Variation)

BIO16 = Precipitation of Wettest Quarter

BIO17 = Precipitation of Driest Quarter

BIO18 = Precipitation of Warmest Quarter

BIO19 = Precipitation of Coldest Quarter

This scheme follows that of ANUCLIM, except that for temperature seasonality the standard deviation was used because a coefficient of variation does not make sense with temperatures between -1 and 1).

You may also use Worldwide Bioclimatic Classification System link in which you can search by country. The categorization of the summer, winter and etc. for each country is specific:

Using this web you can get the following parameters:

Country name, station location and elevation, station name

The length of the observation period for temperature and precipitation respectively

Annual average of temperature and annual precipitation sum

(red) Temperature curve

(blue) Precipitation time series

Indication of frost periods

Mean daily max. temperature for the warmest month

Mean daily min. temperature for the coldest month

Hoping this will be helpful,

Rafik

Dear Amrit Thapa,

The following documents may be useful for you.

I hope it helps. All the best !

Its natural tendency to have more small drops than the larger ones. We can check concentration if u share u r plot. 

compare u r plot of size distribution with a plot from this paper

Mr. Abu Mohammad Azmal, Your idea is very fine but this is not man made, we can not go against nature. Salinity of sea water is natural, we can desaline a small portion of sea water for our use. For our comfort, we are burning fuel to generate electricity, this process produces carbon dioxide a green house gas, this prevents atmospheric heat radiation to outer space that warms our environment. Generation of electricity also utilizes only maximum 45% of heat in thermal power generation to convert into electric power, balance 55% is left out and added to global warming. Human activities for industrialization, mining, deforestation etc are main causes of climate change on earth's surface.

Good morning Gabriela,

In general the reanalysis data bases, haved data of temperatures of soil is necessary determinate your interest area.

the names of possible data bases for utility are CFS NCEP-NCAR, ERA 40 and ERA Interim

The following ppt and paper may help you.

Dear Subash, 

Weather observatories in India record daily rainfall at 08:30 am.

Total rainfall for the preceding 24 hours ending at 08.30 am of the recording date of the measurement is recorded as daily rainfall. It should be taken as 24-hour rainfall recorded on dd/mm/yyyy at 08:30 AM.

For example: Rainfall received from 08: 30 AM 10/07/2016 to 08:30 AM 11/07/2016 is recorded on 11/07/2016. 

IMD never reports the 24 hour rainfall as today's or yesterday's rainfall. It is just the rainfall recorded at 08:30 AM on some random day.

Though you need not to but you can visit your nearby IMD observatory sometime to verify this.

--------------

Therefore to your doubt "The cumulative rainfall from yesterday 7-July-2016 8:30 AM to today 8-July-2016 8:30 AM if 50mm is it for yesterday or today?"

It is neither today's rainfall nor yesterday's rainfall, It is 50 mm rainfall received from 7-July-2016 8:30 AM to 8-July-2016 8:30 AM. 

I am not sure about WMO guidelines but this is how IMD weather observatories record rainfall. 

To add more, places like Cherapunji where it rains cats and dogs these measurements are made much frequently; sometime at 3-hour interval instead of 24 hour to avoid overspill from raingauges.

Edit: Reference Line 178-183

@Ariadne Tsambani, Good day. Thank you for your kind response. Its really appreciated. Any other information regarding  the energy consumption rate of climatic chambers are welcomed. 

Regards

Kwesi

I agree with Cassie, the question is too broad and doubt you will get the best response. You need to be more specific about your intention and focus while asking such question. That said, speaking from an African context, institutional interaction is often generally underplayed when dealing with policy formations (such as policies on adaptation and mitigation). In most developing african countries, there is always a continues interaction between formal and informal institutions, which often leads to evolution of new institutions that are the actual institutions in use by the stakeholders (what you refer to as organisation). 

There are very different type of products for estimation and monitoring the precipitations. You could use TRMM data.

Good luck

Eyad

Dear Muhammad,

I use R statistical program. Although I don't know if you use R, I am posting a script I have developed which gives sums of monthly rainfall from your data. It may be useful for others also who may need a quick way of doing this. It is appended/pasted below..

setwd("D:\\IMTR\\AMTC16_STUDENT PROJECTS\\Victor_Bible") ## set your own directory/path in your computer drive where you saved your data--Data_solved--

yourdata<-read.csv("Data_solved.csv",header=TRUE) ### Note and Ensure you save the Data_solved file in comma seperated (.csv) excel format

head(yourdata) # to view your data

##we are interested in Column 2, 3 and 4 aka Year, Month and Value

# You can use the function tapply, list and sum to get your solution as indicated below

monthlysum<-tapply(yourdata$Value, list(year=yourdata$Year, month=yourdata$Month), sum) # this function sums the dailies into monthly totals in each year

monthlysum<-as.data.frame(monthlysum) # convert to a table

head(monthlysum) # view the out put

write.csv(monthlysum, "Monthlyraintotal.csv") # rename and save the final out put in the working directory as "Monthlyraintotal"

Hello Chetna,

I like Mostofo Kamal's suggestion.  There is a large network of radiosondes that I frequently use as well from time to time.  It may be that there are none near you though. I don't have any immediate suggestions, however, I do have a consideration.  Although you are performing a monthly average, it would be wise to keep track, if possible, of any low-level inversions that could skew the result.  I'm saying this because you are using only three points.  It may be that you do, in many cases, have the environmental lapse rate you are looking for, but that the three points you are looking at do not fall along that constant lapse rate temperature line because of the presence of an inversion.  Just a thought.  

Please find attached how to calculate the performance ratio 

Downscaling is not a interpolation technique.

The results you obtained for the comparison are quite characteristic; reasonable differences for temperature and large differences for (monthly) rainfall. Differences for daily rainfall will be even larger. The spatial variability of rainfall is much larger that that of temperature making GCMs less suitable for rainfall simulation. Most rainfall generating mechanisms are not explicitly solved by GCMs but rather parameterized and this often results in a poor simulation of temporal and spatial variability of rainfall at monthly scales and even more at smaller temporal (and spatial) scales.This is a smaller problem for the temperature, also because the energy balance in the primary focus in GCMs rather than the water balance.

Downscaling, often in combination with bias correction, is therefore a must for most impact assessments using GCM data as source data. Also because data from one (GCM) grid cell should preferably not be used in an isolated way, but rather patterns from GCMs should be used for impact assessments. If dynamical downscaling using a regional climate model is not feasible, statistical downscaling can be a good alternative.

Hope this helps,

Martijn

Its great. Thank you very much. Do you have any research paper related to this. i would like to request for related papers.  Can you please share these information.

Have the questions been asked and answered?

For some history please see:

Good Luck

Upwelling, such as that typical in eastern boundary current regions (California Current, Peru-Chile Current, Benguela Current), brings relatively cooler water to the surface. This creates cool, moist air over the ocean surface. If this air moves over the relatively warmer land mass, the moisture condenses and creates fog.

There is a natural onshore flow that develops in response to the warm thermal low that typically sets up over the land mass adjacent to the eastern boundary, such as in the SW US. This pattern draws cooler marine air over the land, resulting in a persistent foggy condition during the local summer (high upwelling and warm continent). Local vegetation can adapted to this condition, and agriculture interests take advantage of it.

One interesting area of study is how anthropogenic warming will affect this pattern. The hypothesis is that global warming will lead to a warmer continent AND greater upwelling-favorable winds, which may result in greater fog. I am not aware of any substantive study to confirm this. One problem with looking at historical records of fog and SST is that other factors contribute to fog in coastal regions, and the fog is spatially very heterogeneous and localized.

Hi

There has been study by T. N. Krishnamuri ( Monthly Weather Review September 1983) on the dynamics of heat low. He studied the dynamics of heat low over Saudi Arabia using MONEX dropsonde and radiation measurements. Heat low is maintained by subsidence from the higher levels. Thar desert dynamics is similar to that. More details can be seen from the paper. 

J. R. Kulkarni

Dear Getnet Feyissa,

The answer to your question depends on the following:

To considering all the above conditions in a combined way (if the cities you are talking about are within or near the Lake Victoria Basin located in the central part of the East Africa) , I refer you to the most recent paper published some few days ago prior to the time of your question. Access the paper via this link (below) referring to "Onyutha et al. (2016) Comparison of different statistical downscaling methods for climate change rainfall projections over the Lake Victoria basin considering CMIP3 andCMIP5." or download the attached file.

Best wishes

I think more efforts should be taken for mitigation...... adaptation sometime may be natural.... If we gave more importance to adaptation this means we are neglecting the causes of climate change. And everyone's capacity of adapting is different than those who can not able to adapt with change what is the solution? can we leave them to die or suffer?

Another argument: https://www.linkedin.com/pulse/carbon-energy-balance-cropping-systems-karuppan-sakadevan?trk=hp-feed-article-title-comment

This question is very important, also depends on the type of study, whether point based or regional based. If point based, straightaway can be taken for that location and if on regional based, you have to delineate land units within the region on the basis of bio-physical (soil general characters and problem soils, weather normals-seasonal or annual) and socio-economic (admin boundaries, irrigated areas etc) and then run the model for those land units and within the land unit the respective weather stations are identified.

you can refer 'Alternate land use options- Haryana case study' a study carried out by our group at IARI, New Delhi, India

Instead of trying to tease out climate change on a local scale, perhaps it would be easier to determine what factors previously were acted together to produce a climate stable-state in a particular area, prior to any recent climate change conditions.

For example in California we had regular rainfall between October and April each year, that originates from the Pseudomonas host tropical native forest trees growing in Indonesia and Malaysia, as does the India Monsoon originate from that area also each summer.  

So if you trace back the source of Mongolia's rainfall origins, you may find that some on-the-ground changes in distant lands are actually the main driver of what is viewed as climate change locally, like increases in local droughts.  

For California we have the cutting of tens of millions of acres of the tropical forests and converting them to oil palm farms, that were the source of our rainfall in the Pseudomonas bacteria living on their leaves over the last decade, which has caused a ten-year drought to engulf our State.

 Our fifth largest river, the Salinas, has not flowed to the ocean in three of the last four years, for example.  But if just looked at the climate change in California on the local scale, and not tracing back where our rainfall originates, we would miss the massive tropical forest changes occurring many thousands of miles away, that are driving the changes. 

Hello, at the European Commission, we are currently working on that, please take a look to :

Lavaysse, C., Vogt, J., and Pappenberger, F.: Early warning of drought in Europe using the monthly ensemble system from ECMWF, Hydrol. Earth Syst. Sci., 19, 3273-3286, doi:10.5194/hess-19-3273-2015, 2015.

Adaptation refers to those crucial actions or plans that a commuinty or household or individual will employ against a current or anticipated impact of climate change whilst resilience refers to the ability to recover (bounce back to the original state before the exposure to shock) from the effect of climate change.

Hi, Pierluigi:

I think it´s not only the kind of instrument, you also have to be sure about the answer of the instruments, beause if you put them into crops there will recived the influence of the turbulence, I recomeded you to find assistance with a Meteorologist.

As a modeller I found data on soil carbon, its changes with time, soil bulk density are lacking.

We usually get help from long term fertilizer trials datasets; unfortunately these are inadequate and heavily skewed towards crop data.

Soil data are few and far between.

Regards

no submission 

Dear Muhammad, RCP4.5 and RCP8.5 are different representative concentration pathways corresponding to different  possible scenarios, all of which are considered possible depending on how much greenhouse gases will be emitted. Regarding the period you are interested in, the RCP8.5 project a  global warming of about 2deg compared to the 1.5deg in RCP4.5 (this is a CMIP5 models ensemble average). If you want to focus on small spatial  domains (you mentioned 200km)  you could decide to get the 10 models with the higher resolution providing both scenarios: 

CCSM4,  

CMCC-CMS

CMCC-CM

CNRM-CM5

HadGEM2-CC

HadGEM2-ES

INM-CM4

IPSL-CM5-MR

MIROC5

MRI-CGCM3

enrico

there are some workers in 'meteoroloji genel müdürülüğü/ANKARA, goverment office' for turkey. And prof. Murat türkeş as scientist interests in climate change in middle east technical  universty.

Hi Alastair,

Are you sure about the magnitude of global emissions. Do you think that for example China, India, Bangla-Desh the majority of African countries, Russia or North-Korea and many other countries, produce reliable emission date? In some of these countries emissions of CO2 are even kept a secret if these countries even make inventories at all ! I have the  strongest doubts on that!

That's why I leave out global ermission estimates, because they are - according to me - utterly unreliable, at the global scale, I think even their magnitude is underestimated severely. A small minority of countries does a good job though, but we are dealing with a global phenomenon here. Hence?

in the interpretation of Mauna Loa data - because that's what we are dealing with here, how do you corroborate the one third  - one third rule for CO2 absorption in oceans and terrestrial vegetation. This would mean that 60%  of all CO2 emissions would be taken up again.Well the terrestrial science community  and remote sensers are still trying to accurately pin down the uptake of CO2 by vegetation. The same for the oceanographers and CO2 dissolution in oceans and the subsequent uptake by phtytoplankton, and that with oceans which acidify?  CO2 oceanography is a field of research which is even more complicated than the terrestrial one, especially to get a grasp on the CO2 uptake figures by those systems. I think you are strongly underestimating the complexity of these systems Alastair. Hence, Are you not speculating a little but too much collaegue and introducing some very crude assumptions and rules of thumb?

Figures and facts that's what we need, not the mumbo jumbo of rules of thumb. And what is the difference between a runaway process and a process with a strong positive feedback? Semantics or physics? Please man!

CHeers,

Frank

Setting all aside which is nonlinear and difficult to treat, and what either statistians or modellers who prefer deterministic (primitive or conceptual) modelling failed to properly describe in their terms, what remains are linear, statistically solvable problems. Bravo! If I lost a key in the dark, I will seek for it in the light circle of the street lamp - and probably come to the conclusion that I did not loose a key at all. Luisiana, have a look around to see that the environment we live in is full of structures that emerge from elementary rules of local interaction but can hardly be expected to exist at this level of description. If one wants to label it this way, it appears to be the "unreasonable effectiveness of mathematics in physical sciences" (Wheeler), which bears this higher level of structuring and description - among the mathematical disciplines notably number theory, which is 'unreasonably effective' in describung synchronization phenomena (low rational frequency relationships, for example, at top of the Farey tree of rational numbers). Cf. the paper of Lagarias ("number theory and dynamical systems") which I like to cite in this context.

Concerning GCMs, they contain all the ingedients needed, but - you're certainly right in this respect - may become intractable (or at least difficult to handle) when chaotic dynamics, homoclinic or heteroclinic orbits, etc. emerge. The "solution" of fixing the srews that parameterizations offer until dynamics become tractable just leads to the problem that I have quoted (as an example) of inadequate monsoon simulation. I can demonstrate this with the "small" GCM that I have run in the past in order to better understand these dynamics (a Mintz-Arakawa GCM). I have learned a lot when using this model, even of its failures galore (instabilities) I had to struggle with. I could not have developed my conceptual view on boreal summer monsoon dynamics without experimenting with that 'old-fashioned' model. So, at least for me, it was very helpful to have such a tool near at hand (which could be run even on a 386 PC at the beginning of the 1990s).

A nonlinear system of equations bears the corresponding linear solutions as well, and if the situation (initial and boundary conditions, parameter values) is such, it will find them - but reducing it to linearity in cancelling the nonlinear terms, you will never see the nonlinear solutions, of course, and thus perhaps come to the conclusion ... the key has not been lost :-) (my apologies). The problems I quoted of present-day large GCMs appear (to me) to be a result of "fixing the screws" until the models become tractable (an economic decision as well), and losing the interesting nonlinear dynamics this way that at least qualitatively correspond to the behaviour of mother nature.

Kind regards,

Peter

Climate science is a branch of the natural sciences or more accurately a group of disciplines ranging from astronomy through physics to zoology, a true A to Z of scientific disciplines .  Understanding the changes in climate requires a huge multidisciplinary approach since no single cause can be observed and the range of effects is truly enormous.

The problem however with the political and social science involvement is that in politics vested interests and ideologies often overwhelm the 'real' science.  While the effects of natural science impact hugely on social sciences here, such as in designing strategies for coping with climate change the politics is more disingenuous.  

Politics and science have always been a difficult match.

There is also a set of indices that was developed for the measurement and characterization of climate variability and change on a global scale with particular focus on extreme events. For more details see the information provided by the Expert Team on Climate Change Detection and Indices (ETCCD): http://etccdi.pacificclimate.org/indices.shtml.

An overview of the indices is given in this paper: http://onlinelibrary.wiley.com/enhanced/doi/10.1002/wcc.147

And this one provides more details on how to calculate the indices and available software as well as on how these indices are represented in climate models:

Dear Muhamad

That depends on what do you want to do and the purpose of Shapiro-Wilk test. I assume you want to check on the normality of your data. SW test is a test of normality in frequentist statistics. Then, will allow to make such diagnosis, and then, proceed with other tests for bivariate and/or multivariate analyses which relies on the normality assumption, e.g. simple linear regression.

If you want later to compare two set of data, the same is considered, if establish a normal dsitribution, you may would like to test mean differences using e.g. a t-Student test.

Finally, please if you consider this answer appropriate, please upvote it using the green up arrow click. Thanks.

Our group at NCAR does a significant amount of Regional Climate Downscaling with WRF. Here are two articles that might help:

Done J.M., G.J. Holland, C.L. Bruyère, L.R. Leung, and A. Suzuki-Parker, 2013: Modeling High-Impact Weather and Climate: Lessons from a Tropical Cyclone Perspective, Climatic Change. doi:10.1007/s10584-013-0954-6

Bruyère C.L., J.M. Done, G.J. Holland, and S. Fredrick, 2014: Bias Corrections of Global Models for Regional Climate Simulations of High-Impact Weather. Climate Dynamics, 43, 1847-1856. doi:10.1007/s00382-013-2011-6

We have also just completed a project to convert the CESM AR5 data to WRF Intermediate file format. We have also bias corrected this data. All the data are available here:

    http://rda.ucar.edu/datasets/ds316.1

The source code used will be make public soon, on the above web site. We are also in the process of generating a technical document describing the methods and code. The document will also be on the above web site when completed.

Joanne, The articles cited above (various John Kutzbach publications; Schmittner et al 2011; and Oster et al 2015) are excellent suggestions. Another article that may be of interest to you, recently published in Climate Dynamics, is: doi:10.1007/s00382-015-2502-8; "Higher Laurentide and Greenland ice sheets strengthen the North Atlantic ocean circulation" by Xun Gong et al. 2015. Model simulations of various glacial and interglacial climates are conducted with the goal of identifying different [North Atlantic-based] surface-wind patterns (and associated ocean circulation patterns) that evolve under different profiles of ice-sheet topographies. Marcia

One answer is that you cannot be sure. But as a rule of thump we tend to use at least 3 GCMs to make a meaningful assumption.

Regards, Levent

I got a clue about the Pseudomonas in the India-Pakistan area and the monsoon from Alexander Frater when he chased the India monsoon in his book “Chasing the Monsoon” in 1987 and wrote a diary of that year.

On June 2 he attended a meeting of Mrs. Das’ anti-deforestration committee, where he writes: …”India was once a sylvan country. When Alexander the Great invaded in 327 BC he encountered dense, close-canopied almost impenetrable forests. But peasants were already pursuing a slash-and-burn policy…Trees play a crucial role in the monsoon cycle. By SEEDING clouds they encourage the rain to fall; by trapping it they help recharge the aquifers and hold groundwater in store for the common good.”

I highlighted the word seeding, because the Pseudomonas living on the trees creating the monsoon rainfall was unknown by science in the 1980s, but the local people knew something like cloud seeding was going on, from their observations.

Thanx !

I'll do it definitely.

Hi,

good question. This depends on what kind of data you want to have.

And always keep in mind that the term "aerosol radiative forcing" only applies to changes in the Earth radiation budget introduced by anthropogenic aerosols.

Do you want to have a global map of radiative forcing values or do you want to compute the forcing yourself from maps of aerosol concentrations?

If you want to have maps of aerosol radiative forcing, you can for example take output from global aerosol climate models, such as those in the AEROCOM intiative. (See link below). However, you have to keep in mind that aerosol radiative forcing strongly varies from model to model, so it might be best to look at the model mean.

If you want to compute the aerosol radiative forcing yourself, e.g with the use of a radiative transfer model, you could take a look at this current aerosol climatology:

Kinne, S., D. O’Donnel, P. Stier, S. Kloster, K. Zhang, H. Schmidt, S. Rast, M. Giorgetta, T. F. Eck, and B. Stevens (2013), MAC-v1: A new global aerosol climatology for climate studies, J. Adv. Model. Earth Syst., 5, 704–740, doi:10.1002/jame.20035.

You can also take a look at the most current IPCC Assessment Report, Chapter 8.

Hope this helps!

Cheers, Karsten

You did not specify the efforts you already made, nor the bands you displayed and the way you diplayed them, which makes difficult to give a precise answer.

For a start: differences can be due to actual differences on the ground, e.g. adjacent images were recorded on different dates, in different seasons or because of atmospheric differences. Step 1 would be to try and have images from the same year and/or same season and do atmospheric correction. If that is not possible or if differnces still remain, it depends on the purpose what you could do. If the purpose is classification, I would classify the images seperately (with their own training sets) and join the results. If you want a mosaic for visual interpretation or for an overview, you coud use histogram adjustment and stretch each band between the same corresponding values of the same band in the adjacent image. In other words: take one reference image, find the minimum and maximum for each band and stretch the corresponding bands of teh adjacent mages between teh sam values. This should give a visually more pleasing result, provided there are no large differences in land cover (and in shape of histogram) between the adjacent scenes, but it could introduce errors if you want to use this in classification. Soem image procesing softwares have algoritms for mosaicing in which color differences can be smoothed. 

These extreme weather conditions are all linked to man's destruction of his own habitat, the earth., through his various activities driven by greed and selfishness

I agree with the comments given previously:

These are modelled winds - based on computed temperature / pressure fields.

the real wind field would be more complex.

This is particularly so when the pressure level or altitude modelled is below the maximum altitude of the near-by terrain (i.e. very high mountains)!

Physically, friction with the ground surface causes a rotation of the wind vectors at low levels, away from the Coriolis wind field found at higher altitudes (more from high pressure to low pressure - rather than perpendicular to the pressure gradient.

Other important and real factors are that water vapour distributions are very complex and create vertical pressure distributions that are more complex than the models can properly assimilate - due to lack of real high-resolution global measurements.  Increased (vertical) complexity of the real pressure distributions will cause real vertical variations of wind speed.  However, detailed measurements, not assimilative models, would be required.  As yet, the global measurement capability is not available to fill this requirement!

There is an open  meeting of the Royal Meteorological Society to discuss the troposphere stratosphere link later this month. See: http://www.rmets.org/events/stratosphere-troposphere-coupling-earth-system-where-next

Patrick Druggan is right. Dry the samples in a dry warm place and it should work.