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Can I find a reference on artificial intelligence in climate science?
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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
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I want more research experience on the use of artificial intelligence in climate science with the methods used for application
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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.
* United Nations Framework Convention on Climate Change (UNFCCC): The primary international treaty dealing with climate change.
Governmental Agencies
* NASA Climate Change: Provides scientific information and resources on climate change.
* National Oceanic and Atmospheric Administration (NOAA) Climate.gov: Offers climate data, information, and tools.
* Environmental Protection Agency (EPA) Climate Change: Provides information on climate change impacts and solutions.
Research Institutions
* National Center for Atmospheric Research (NCAR): A leading nonprofit research center focused on atmospheric and related Earth system sciences.
* Woods Hole Oceanographic Institution: A private, non-profit research and education institution focused on marine science.
* Lawrence Berkeley National Laboratory: A U.S. Department of Energy national laboratory specializing in energy and environmental science.
Non-Governmental Organizations (NGOs)
* World Wildlife Fund (WWF): A global conservation organization working to protect the planet's natural environment.
* Greenpeace: An international environmental organization working to protect the environment and promote peace.
* The Nature Conservancy: A global conservation organization dedicated to conserving the lands and waters on which all life depends.
These organizations provide a wealth of information, research, and resources on climate, including methods, means, and strategies for addressing the issue.
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Monitoring climate change requires accurate and reliable atmospheric data to understand and predict changes in weather patterns, temperature, and other critical variables. This question aims to identify specific sources or platforms that provide atmospheric data essential for climate change research and monitoring. It seeks to explore the most trusted and widely used data sources, such as satellite missions, ground-based observation networks, and climate models, and to understand their strengths, limitations, and applications in climate science. The goal is to provide researchers and policymakers with information on where to obtain high-quality atmospheric data to support their efforts in studying and mitigating the impacts of climate change.
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Yes, there are several specific sources for collecting atmospheric data used in climate change monitoring. These sources include:
  1. Satellites:NASA's Earth Observing System (EOS): Provides comprehensive climate data from instruments on satellites like Aqua, Terra, and Aura. ESA's Copernicus Programme: Specifically, the Sentinel satellites, which provide data on atmospheric composition, sea level, and land use. NOAA's GOES and POES: Geostationary and polar-orbiting satellites that provide continuous weather and atmospheric monitoring.
  2. Ground-based Observatories:NOAA's Mauna Loa Observatory: Famous for its long-term measurements of atmospheric CO2. Global Atmosphere Watch (GAW) Stations: Operated by the World Meteorological Organization (WMO) to monitor greenhouse gases, aerosols, and other atmospheric components.
  3. Aircraft Campaigns:NASA's Atmospheric Tomography Mission (ATom): Collects data on atmospheric composition over the world's oceans. NOAA's Hurricane Hunter Aircraft: Collect data on atmospheric conditions in and around tropical storms and hurricanes.
  4. Weather Balloons:NOAA's Radiosonde Network: Provides vertical profiles of temperature, humidity, and wind through the launch of weather balloons.
  5. Buoys and Ships:Argo Program: Deploys floats that measure temperature and salinity in the upper 2000 meters of the ocean, contributing to understanding ocean-atmosphere interactions. Tropical Atmosphere Ocean (TAO) Array: Monitors sea surface temperatures and provides data crucial for understanding phenomena like El Niño and La Niña.
  6. Climate Models and Reanalysis Data:ERA5 Reanalysis from ECMWF: Provides a comprehensive dataset of global climate data, combining past observations with models. NCEP/NCAR Reanalysis: Another key resource offering long-term climate data synthesized from various sources.
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.
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  • How do artificial intelligence and machine learning contribute to climate modeling and prediction?
  • Provide examples of successful applications of AI/ML in climate research and analysis.
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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.
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I bring you my experience with ANN. I published in 2009 and later some articles where I perform downscaling through ANN for data from IPCC models. In the beginning it was very complicated to publish it, because the academy did not agree with the theme, even so, we managed to publish and the results. My perception is that nowadays the use of ANN has become much broader and with several applications in climate science, so I would like to know from you what are your perceptions regarding the use of ANN in climate science?
best,
David
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To predict the variables - some sort of forecasting.. Even it can be applied to know the Environmental health in future
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Hi Guys!
I usually download future climate data from Worldclim.org.
Their website says that "Data at 30-seconds spatial resolution is expected to be available by the end of March 2020", however, this has not materialized . . . https://www.worldclim.org/data/cmip6/cmip6climate.html
Does anyone know of alternative sources to download future data at this (1km) resolution?
Many thanks!
Joshua
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Hi Guys
Update, Worldclim has updated their website with new CMIP6 30arc sec variables.
We have waited long enough.
Enjoy!
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It is generally accepted that Milankovitch cycles had influenced paleoclimate. Is there any connection between greenhouse effect and variations of the earth's orbital parameters?
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I think these papers could help you;
  1. On the correlation between Earth's orbital perturbations and oscillations of sea level and concentration of greenhouse gases, June 2015,
  2. High Frequency Variations of The Earth's Orbital Parameters and Climate Change, September 2002,
They're available on the ResearchGate Server for download.
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SST provides a measure of the warmth of the upper ocean, while the depth of the warm water is related to the thermocline depth (D23).
From the above line does D23 represents thermocline depth?
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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.
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Is there already literature about that issue? Maybe regarding to Mignolo´s concept of 'the colonial matrix of power'?
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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.
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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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Scientists are leading the world in medicine,pharmacy and industry,but normal person cannot follow easily,therefore scientists need more elaboration to convince others,
The latest survey data from Yale and George Mason universities underscores the partisan divide on climate science denial – 73% of Americans realize that global warming is happening, including 71% of liberal/moderate Republicans, but the average is dragged down by the mere 47% of conservative Republicans who answer this question correctly. On the bright side, this is a big improvement from the 28% of conservative Republicans who realized global warming was happening just two years ago.
Similarly, 56% of Americans realize global warming is mostly caused by humans, including 49% of liberal/moderate Republicans, but the number is again dragged down by the 26% of conservative Republicans correctly answering this question.therefore how scientists be close to public?
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I think this topic is very important for health and safety of millions ,
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I wanted to observe the rotational velocity of the Hurricane which was reported to be around 175mph, but when I looked at reanlysis products like ERA Interim or 20CRV2C the maximum wind speed I can get is about 45mph, why is the speed in the reanalysis products so low. Or is just that they are measuring different speeds?
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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?
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Dear Researchers,
I would like to ask that Thiessen Polygons method is useful for temperatures and precipitations analysis in high elevation areas approx. 3000-4800 meter m.s.l. The area is mainly
mountainous.
Regards
Naveed.
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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.
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The intricacy of Tectonic vs Climate change in the formation of the land-forms is revealed by the events of landslides, mud and debris flows during the Thursday's 6th Sept 2018 earthquake ( 6.7 Magnitude) without any rainfall in the Hokkaido Island. Now heavy rain was expected in Hokkaido through Saturday, and the Japan Meteorological Agency warned of more mudslides, saying earthquakes of a similar intensity could occur again in the region ( KYODO NEWS - 5 minutes ago - 13:30 hrs 7-9-2018 ). Now we have seen the tectonic activities in this parts of the island which has shown lots of damages and disasters due to quake debris, again it may be followed by climatic events i.e., precipitation (rain) which may carry away more debris into the channels by forming new gullies and tributaries channels which may be producing debris flood. So, the impact of tectonic and climatic activities are facing by the present day civilization in the hilly region of most part of the world. It is time to correlate the source of tectonic as well as climate change being a geo-scientists which are dominantly single minded in terms of climate science and tectonics studies.
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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. . . “
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Hello all
There is a vast amount of literature, some very old indeed, that talk about solar irradiation component modelling. I want to break global measurements (in W/m2) to its direct and diffused constituents. What other climatic measurements do I need and can anybody offer a simplified method?
Thanks
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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).
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If the proposition is accepted, it represents a major cultural change with implications for the academic rewards system, training, and perceptions of prestige, as well as opening up a new professional role of 'intermediary / broker' executing analysis, synthesis and co-production of policy and decision-making in a structured and organised manner - as opposed to the fragmented and ad hoc efforts occurring currently.
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Ken,
The Cambridge English dictionry defines mild as:
Mild weather is not very cold or not as cold as usual:
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.
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As we know, some island countries like Tuvalu, Kiribati and the Maldives risk disappearing because of the climatic changes. Do you know of any solution adopted by island countries to avoid their disappearance caused by climate change?
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In case of such situation, the affect countries should have plan to accommodate citizens in friend countries.
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We know that such a condition allows the outward propagation of internally generated disturbance from the model area and which can communicate with the tides.
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Thank you
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Role-Playing Game is a tool to explain factors and decision making. This game is an appropriate methodology for analyzing farmers' behavior?
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Yes,  It works well. Have tried it quite successfully in rural settings.  
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I need to improve/increase the spatial resolution of gridded climate datasets from low to high (eg., 5 km to 90 m) for Himalayan Catchment. Can you suggest some?
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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
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Could anyone recommend a satellite derived product of cloud cover on a subdaily scale? Many thanks!
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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.
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I am looking for the effect of hot climate on the behavior of transit use and public transportation ridership? and  what is the human thermal comfort in outdoor spaces in a hot arid region?
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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
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I'm thinking of using Precipitation Runoff Modelling System (PRMS) model to simulate and predicts climate variability and river discharge relationship, but I may wish to have more suggest as to the best model to use in this study
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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.
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need to calculate aridity index of Oman by knowing annual rainfall
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Hi everyone,
Can you help me to understand the quarterly categorization of bioclim data? The bioclim data has periods on coldest, warmest and so on. But, which months are coldest or warmest? Is it uniform all over the world? I mean, the months, which are driest in USA, is same in Bangladesh?
Thanks in advance.
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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
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What are the issues that could arise by distribution of point observation of precipitation and temperature in Himalayan catchment using lapse rates and precipitation gradient ?
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I have 2DVD raindrop size data which is showing  high concentrations(almost 90% total concentration) of droplet diameter below 1.5mm. Can someone please suggest whether these observations are accurate?
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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
Raindrop axis ratios, fall velocities and size distribution over Sumatra from 2D-Video Disdrometer measurement
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Root Reason behind the World Weather Change:
If we look around earth’s environment along with the space we find the world is surrounded by three main atmospheric layers and the function /usefulness of these layers are to act as filter to protect the earth from many hazardous rays of the sun and also from the other disaster elements of the space by the way to make the earth suitable for living beings. I would like to introduce here the main concern about the atmospheric layer is about its dual activities first one as the filter of the earth and the second one is the atmospheric layers act as the shed or boundary to keep the earth’s environmental condition suitable for living beings here. The layers also restrict the mixing of the earth environment with the more hazardous space environment. 
If we look to the components and the composition of the earth surface we find more than two third of the total earth’s area is occupied with liquid component mainly with water, that is also main component of living cells constituents. Among the existing total amount of liquid on the earth surface most amount are saline water in the oceans, seas and in the gulf. Rest water is sweet but having the same properties of polar nature in the sources of rivers, cannels and in the ponds. My main attention is on the fact of salinity of the water sources on the earth surface and their role on the total earth environment.
In the saline water, metal ion is the main constituent’s components that are forming ionic/polar bonded compounds with halogens. The situation is that, most of the existing liquids on the earth’s surroundings are in polar form which is in term in the form of partially ionic condition. If we deepened our concern the earth surface is accompanied with abundant of ionic (polar) components and how the situation was made stable on the earth surface that was suitable for living beings existence with suitable environmental climate conditions.
The fact of the gradually losing electrolytic balance can be pointed out: the use of the electrolytes by living beings over million/billion years through the total mass of the earth surface and it’s surrounding. Besides, the sweet water percent is also increasing for the last few decades by melting snow and freezing water from the storage of the end corner of the earth along many points of the earth due to the raise of the overall temperature of the earth surface.
 All living beings collect their required energy to lead their mode of life using the available sources related with the earth existing balancing system. By the way the earth is losing (directly or indirectly) the balancing properties (electrolytes) from the existing suitable conditions on the earth surface.  Inventions of various new scientific equipment using the available electrolytic sources from the earth also resulted a great loss of the total electrolytic balancing system. Moreover the radioactivity of many elements both natural and artificially causes the serious damage to the total electrolytic balancing systems across the earth environment. Even the productions of various new living organs inside the living being using the earth sources causing the deficiency in the total available balancing systems across the earth. These are all spontaneous process and are increasing more rapidly day to day with the modernization which is much alarming to the loosing of total electrolytic balancing system across the earth. From these above facts it may mentioned that the world existence is under threat due to the electrolytic imbalances in and around the earth environment.
Suggestions for recovery:
 Answer may come by the way that there are must an exact electrostatic balance between the aqueous electrolytes (sea water) and the existing all other components (ionic, nonionic, covalent, and neutral) inside the atmospheric boundaries along with the components of the mines within the total mass of the globe. There may have other facts like attraction/interaction force i.e., Van deer Waal’s attraction forces, dipole- dipole interactions (associate the molecular level) between the abundant polar solvent (water) around the earth’s surface and between other components within the range of the earth. If we come to the main concern of the global climate change relating the change of the quality and quantity of the sea (saline) water which is effecting on the total electrolytic purity/quantity, causing the electrolytic imbalance inside the atmospheric boundaries around the total mass area of the earth. The fact of lost electrolytic balance is the main reasons for the unusual behavior of the climate around the globe. This reason also may responsible for unique happenings in the space near to the earth.
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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.
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I found data in some papers (mostly concerning marine turtles sexual determination), but I can't find a monitoring of that for a extended period of time (a year, e. g.).
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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
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How to calculate future climatic data from GCM Marksim.
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I wish to do a validation of TRMM-3B42 rainfall at daily timesteps. I have hourly rainfall data from some and daily rainfall from few gauges in my watershed. 
The question is when deriving daily rainfall from hourly records the cumulative 24 hours value should be reported to yesterday or today?
For example :
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? 
What are IMD and WMO guidelines?
Similarly, how to arrive @ daily rainfall from TRMM-3B42 3hr rainfal to match the timings of the gauge. I do understand that TRMM 0hr rainfall is total rainfall in the period 10:30PM - 1:30 AM
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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.
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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
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I am currently investigating the performance characteristics of a 400 Liter Constant temperature and Humidity Chamber under various operating conditions.
Could anyone help me find standard data and procedure for conventional Climatic chambers' performance (Energy consumption under standard test conditions)? if any. 
Any information is highly appreciated. Thank you!!
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@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
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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). 
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I want to download reanalysis data for rainfall, minimum, and maximum temperature for some areas (North Central Province) in Sri Lanka. If there is anyone who knows a data source to download those reanalysis data in grid base for the period from 1970 to 2015. 
Thank you.
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There are very different type of products for estimation and monitoring the precipitations. You could use TRMM data.
Good luck
Eyad
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I have large number of precipitation daily data in a column (for 53 years).I need to convert it to monthly sum. How can I do that? The data available format and required format are attached in this sheet. Kindly guide me
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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"
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I have temperature data of 10 years for 3 stations. I have calculated the month wise average and accordingly lapse rate. i am trying to extrapolate temperature of one station (lower elevation station) to other station (higher elevation station). please tell me how to calculate and validate the temperature lapse rate?
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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.  
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Dear All,
I have one weather station to measure the irradiation every minute. It is said that the measured irradiation can be used to calculate the PR of the PV system. Here PV system means the whole plant.
Since it is in a real world, is it correct to use same irradiation values to calculate PR of each inverter and why ? It seems that it should not be a good idea.
Is there any good method to get the PR of each inverter?  
Thanks.
Bruno 
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Please find attached how to calculate the performance ratio 
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I want to estimate the impacts of climate change on crop yield by 2100. Can I use climate output at nearest point instead of downscaled output ?
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Downscaling is not a interpolation technique.
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I need future climate output, what I have is only climate output at nearest point. What are justifications to use climate series at nearest grid point instead of downscaling GCM output ?
After comparing climate output at the nearest point with observed climate, I found that RMSE of monthly mean temperature is between 0.5 and 1.2 celsius. But, RMSE of observed monthly rainfall (compared to rainfall output) is quite high, about 186 mm.
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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
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Generation of power like hydro power and dam operation changes micro climatic conditions. How other electric power generations including hydro power is responsible to change micro climatic conditions.
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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.
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Which is the most suitable RCM output to study heat/cold waves frequency and intensity in the future?
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Can cloud-seeding technique used by a country cause any effect on cloud formation of its neighbor countries?
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Have the questions been asked and answered?
For some history please see:
Good Luck
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"Cold upwelled water alters local weather. Weather onshore of regions of upwelling tend to have fog, low stratus clouds, a stable stratified atmosphere, little convection, and little rain." from the book "Introduction to Physical Oceanography "(Robert H. Stewart). If the upwelled water in the coastal area is relatively warmer than ambient water, how does it change the local weather?
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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.
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Heat low strength is critical in determining the magnitude of South west monsoon in Indian sub-continent. 
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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
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I am trying to downscale statistically from general circulation models and access the impact of climate change in pertaining to urban flooding and urban heat Island.The aim of the study is to advance the resilience planning of Addis Ababa City, However, as far as I have read most of the general circulation models are downscaled for agricultural applications and predictions.There are various models all over the world from different climate centers, and I need to know the recenetand best models that can statistically downloaded under rcp scenarios and applicable for urban climate change vulnerability assessment.
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Dear Getnet Feyissa,
The answer to your question depends on the following:
  1. Which phase of the Coupled Model Inter-comparison Project (CMIP) you want to deal with (of course the latest generation phase 5 i.e. CMIP5 may be preferred to the CMIP 3)
  2. Which method of downscaling you want to apply (dynamical or statistical method); if say, statistical downscaling (SD) is what you want to go for, which approach of  the SD do you intend to apply specifically?
  3. The purpose of your research or the intended application to incorporate your study findings. Will you need to consider extreme events or the average climate conditions?
  4. Which variable specifically to be downscaled (wind speed, temperature, rainfall, etc)
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
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  In the current situation which is more valuable. 1st we have to adopt ourselves with the climate change or we have to take steps(what kind of step) to mitigate the climate change
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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?
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I strongly think and hope very large scale desalination of sea water to irrigate arid zone is the second way to fight against global warming and its consequences as rise in sea level and accelerated desertification of arid.
Of course, the first is the sustainable behavior; desalination might be performed and funded by ecological means.
In a previous discussion :
Pr K. M.Towe said :"There is no way for "photosynthesis" to "pay" for it's subsequent LONG TERM microorganisms aerobic recycling". I must specify : recycling humans, animals and plants carbon. I wonder how is it possible reforestation would not have any significant effect on carbon footprint and over what time period photosynthesis is much greater than the aerobic respiration of microorganisms.
If respiration of microorganisms always prevailed over that of plants, a long time ago we would have no more oxygen. Do the rainforests have a negative balance sheet of oxygen/carbon?
At a rate of two dollars per cubic meter of desalinated water, what would be carbon foot print over several years of a cubic meters of desalted water in arid and sunny area? That is the question?
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I want to to downscale precipitation and temperature for a basin (6820 km2) using SDSM model. I have 14 station with almost 100% full data (no missings) covering 25 years within the basin.  After review the literature, I get confused whether to select a few representative stations and downscale each one then average their results or average the stations data then downscale. However, the basin is semi-arid with rainy days are very scarce, so poor correlation when downscale each station while averaging stations then downscale showed much more correlation.  So my question is which is proper to downscale each meteorological station or averaged value of all stations in the study area?
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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
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Dear researchers, which satellite data more clearly and feasible at the study of climate change of Asia at the regional level?
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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. 
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I have been working on drought monitoring for several years. I know effective drought prediction is more important than effective drought monitoring from the point view of environmental disaster mitigation. However, I always doubt the predictability of mid-term or long-term drought. Climate models may play some role in telling us the future trend of drought. It is still very hard to predict where and when drought happens.
Thanks!
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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.
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I read some articles which conclude Adaptation and Resilience have the same meaning and in some others, adaptation is just one aspect of resilience.i just want to know what features separate these two topics.  
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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.
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Dear all,
for my study I would like to buy few wind data loggers with vane and speed sensors, would you suggest me some average quality instruments?
I do not need fine-scale measurements, just basic tools.
Thanks
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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.
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When you read scientific research related to climate change it is apparent that many types of disparate data are integrated when simulations of the future are made.  How does the educated, social scientist (with no climatology training ) evaluate the quality of the research and how it is integrated to produce different scenarios with broader or more narrow ranges of values?  Are many researchers focusing on the replication of research?  
How would you rate the state of climate research:  nascent, developing, developed in specific areas, mature?  What are the biggests current gaps in our knowledge of ocean and atmospheric systems...?  Are there also gaps in modeling the interactions between systems that contribute to less certainty?   Does the climate change community readily admit to those gaps?  What are some significant recent anomalies?  Have they been accounted for sufficiently?
How do you judge climate scientists that have gradually evolved into advocates?  
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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
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The Technology Mechanism and Financial Mechanism under the UNFCCC: suggests: the Technology Executive Committee (TEC) and the Climate Technology Centre and Network (CTC&N) Plus Green Climate Fund (GCF).
Has any country established TEC+CTC&N+GCF nationally – to link with UNFCCC?  
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no submission 
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Dear Researchers I am doing a study regarding effect of climate change on a very small catchment. I need future climate data for period 2050-2060s. I have few questions I will be thankful if you share your experience and give suggestions:
1:  How many Climate models exist in CMIP5? What are RCP4.5 and RCP8.5 ?How many models exist in RCP 4.5 and RCP 8.5?
2. I have read there are 40 models for RCP4.5 and RCP 8.5 am i right? Among RCP 4.5 and RCP8.5  if i want to select some suitable models for my catchment what will be the procedure?
3. Dose spatial resolution mater for small catchment having watershed 200km square? 
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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
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I am using a Species Distribution model and want to use Climate gridded data for different scenarios from downloaded Climond. How can I extract the data only for my country of interest? Any suggestions please
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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.
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I recently had  a look at the Mauna Loa dataset.
They are archived and distributed by the NOAA Administration of the US. An intruiging dataset indeed.
I detrended the full monthly dataset (april 1954 till now) and subsequently calculated the yearly rate of change (See figure enclosed). According to me the data suggest that - at least for the Mauna Loa dataset - the atmospheric CO2 mixing ratio has entered a runaway mode (as they say in systems analysis). This would suggest a strong feed-foward mechanism in the processes determining the rate of increase of Mauna Loa CO2 mixing ratio! Nw my questions.
1. Do you agree with my findings (see graph added here as well)?
2. Have other scientists found something comparable to what I find in the Mauna Loa data?
Eagerly waiting (also for the non-believers in climate change) to put your teeth in my hypothesis shortly outlined here and hack it to pieces.
Thanks for looking into the data and findings.
Frank
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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
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It seems in the history of climate science that most scientist spoke of the climate as being stochastic, until about the mid 1980s, where a shift occurred and the climate was more described as being chaotic. Obviously, the shift reflects the wishes of climate scientist to enable prediction and mathematical capture, but it appears only wishful thinking, at best. So, as a survey, what does the RG community think about this fundamental conundrum in the Sciences?
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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
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This question has puzzled me for a while.
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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.
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Recently, many climate indices are known. If one has climate data for a number of years and would like to calculate one of the climate indices, which climate index is achievable with the daily parameters {temperature (Max, Mean, and Min), precipitation, dew point, relative humidity, pressure at sea level, visibility, and wind speed}? and how to calculate that?
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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:
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To conduct climate change detection, I will separate some climate data into 2 periods: the baseline and the current period. Say, climate data will grouped into the baseline period and the recent period. (Afterwards, I will test normality data and check equality of means).
My question:
Shall I employ Shapiro-Wilk (Kolmogorov-Smirnov) test for each data period ?
OR Shall I employ Shapiro test before I separate climate data ?
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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.
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I am working on a project to use WRF-ARW as a regional climate model and  project potential impact of climate change for future periods. But the WRF model doesn't directly ingest the Climate Model out put dat (GCM) from CMIP5   available in ESGF portal directly. So I need some mechanism so as to make this GCM outputs  able to use in WPS the WRF prepossessing system before I run WRF
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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:
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.
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Maureen Raymo hypothesized that the Himalayas radically changed climate by affecting wind patterns by their uplift. I want to know how you can radically change climate by removing a miles-high ice sheet. Hypothetically what would happen to climate if the Greenland Ice Sheet were removed? How did the rapid removal of the Laurentide ice sheet affect wind patterns and climate for eastern North America in the recent past? Has anyone done any modeling of this? Thanks, Joanne
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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
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I know there are no any rules. But I just want to know the community idea about how many will suffice for a study.
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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
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The image from SW coastal India indicates that certain trees in that tropical forest are Pseudomonas hosts of a strain that has a very strong cloud-seeding effect.  Anyone planting Pseudomonas host native trees or shrubs for the purpose of cloud seeding and increasing rainfall downwind?
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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.
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I am interested in understanding the solar irradiance of Tropics during Bølling-Allerød period. I am not able to get the data/curve for the said period. 
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Thanx !
I'll do it definitely.
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It does not matter if the data is a total net forcing or in each individual part.
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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
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Despite my efforts, I still see discordant color differences when I place two or more Landsat8 images side-by-side in a mosaic [1]. For example, one image may be lighter than the other or less saturated, please look to the mosaic. 
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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. 
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The harmattan in west Africa has not been as strong as it is now, and the reports all over Europe, America and some part of Asia, Africa e.t.c have also recorded increased snow, hail storms, flooding and in some cases poor visibility. What is the scientific link of these events to climate change. Proof may be needed
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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
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Dear All,
There are two figures attached that show the isobars of atmospheric circulation on TP. For the green one, arrows indicate the wind field in JJA on the ground (1000hPa). For the other one, there are no arrows (700hPa,equals to ~3000m a.s.l.). Could someone give some advice on why the wind field on TP is different altitude? Thanks.
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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!
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Does anyone know the answer to the above? I was just reading the SPARC July newsletter in which Andrew Gettelman suggested that the tropopause temperature should increase in the future due to climate change because of its effects on cloud in the TTL region. However, many years ago Thomas Reichler and I wrote a paper showing increases in tropopause temperature even for the older CM3-based coupled chemistry climate model runs at GFDL. Broadly, we identified the increase as primarily due to the increased strength of tropical upwelling. This seems to be a robust feature of climate model simulations, related as it is of course to the increased strength of the Brewer-Dobson circulation published by the celebrated author Neal Butchart. Related questions, then, are to what extent do the details of the TTL really matter, and have there been any quantitative changes in climate model predicted tropopause data? Perhaps it's the details of the TTL which give rise to the large scale average that we see in the B-D circulation. Then how good are our models at representing this upwelling change if they are relatively poor at representing the TTL region and cloud microphysics on the scale needed?   
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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
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I am working on rose bengal stained sediment samples and when I tried to deep freeze the sample at -27C it wouldn't freeze because ethanol, which is used for rose bengal staining, doesn't freeze at -27C (Having freezing point -117C). So I have to remove it. If I directly decant it then some ethanol must be in sediment pore spaces and it causes problem to freeze dryer, and if I use distilled water to dilute it, it removes the salt content from sediment  which I don't want to lose.
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Patrick Druggan is right. Dry the samples in a dry warm place and it should work. 
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Recently, I found a new error growth mechanism in climate models due to the incorrectly calculating the concentration of water vapor,carbon dioxide and heat etc under the pre-condition of uniform continuity condition for variables. I have developed an index to identify the non-uniform degree in the fields. But how to move this kind of error remain unknown. Any ideas are welcome to discuss here!
Thanks!
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