by Judith Curry
An exciting new project for my company, Climate Forecast Applications Network (CFAN) to support smallholder farmers in Pakistan and India.
I have been working on a lot of interesting projects for clients of my company. I haven’t written about them on the blog since nearly all of the projects are proprietary (for companies) and for the others I simply don’t have time to write them up. This particular project is a public one, and our clients have written 3 very nice blog posts on the project.
Our client is Precision Development (PxD). PxD is a global non-profit organization that builds low-cost information systems at scale to share knowledge with the world’s poorest and most disadvantaged people. I am extremely impressed by what PxD is doing and their personnel, and we welcome this project as a way to return to CFAN’s roots – our first project was flood forecasting in Bangladesh [link]
Apart from the technological challenges of calibrating weather forecasts with very little observational data, there are some very interesting challenges in working with the PxD team to understand how farmers perceive weather forecasts and how to make forecasts more useful to them.
The idea of providing high quality weather forecast information to smallhold farmers who are living on the edge is important for development and for adaptation to climate variability and change. Operational adaptation, which plans in advance to manage in the face of adverse weather conditions, can be important tool for helping these farmers build some modicum of wealth, and avoid the endless impoverishment of having their crops destroyed by adverse weather conditions. Through this project, we are helping to develop this capability.
Below are extended summaries of three blog posts from PxD:
Weathering the storm: Smallholder farmers and the untapped potential of weather forecasts [link]
Accurate weather forecasts would reduce uncertainty for farmers, yet they are under-supplied and under-studied in developing countries. Government and market failures reduce the quality and reach of weather information in rural regions, reducing its value for households. To address these gaps, Precision Development (PxD) is piloting the provision of improved forecasts to farmers in India and Pakistan. The pilot will investigate how farmers interpret and use weather information, including the cognitive and informational barriers that constrain adoption.
Smallholder farmers live with copious amounts of risk, leaving them vulnerable to income variability and losses. Weather uncertainty is a major source of this risk. A survey of farmers in poor, southern districts of India, for example, found that 73% of respondents had abandoned their crop at least once, in the ten years prior to being surveyed, after misjudging the onset of the monsoon; and one quarter had replanted.
Because weather variability can be disastrous for poor households and is increasing with climate change, economists have dedicated much attention to products that mitigate its consequences, including index insurance and climate-resilient crop varieties. While effective in increasing farmers’ investments and profits, many of these innovations incur high distribution costs that limit their scalability. PxD is therefore exploring an alternative solution for farmers that face increasing climate risk: accurate, phone-based weather forecasts.
Forecasts have a high potential for cost-effectiveness and scale. By reducing uncertainty over future conditions, they allow farmers to make better production decisions throughout the season. Long-range forecasts of conditions over one to three months can enable farmers to make more informed decisions about how much to invest or which crops to grow, while shorter-range products can be used to determine the best time to conduct activities like fertilizer application. These decisions have the potential to generate meaningful yield impacts; PxD’s agronomy team in India estimates that transplanting rice seedlings from the nursery to the field at the right time can generate yield increases of up to 10%, relative to transplanting too late. Moreover, the marginal cost of delivering phone-based forecasts is low relative to other products that reduce risk, meaning that the returns to their provision would increase with scale.
Currently, however, rigorous evidence on weather forecasts’ effects on farmer outcomes is sparse. Part of the reason may be the limited availability of accurate, useful forecasts in many developing countries. The limited evidence base suggests that these quality shortfalls reduce forecasts’ value for farmers. Short lead times and a lack of complimentary advice may further explain the lack of impact.
PxD’s new pilot aims to build the evidence on the benefits of improved forecasts for smallholders. We have partnered with the Climate Forecast Applications Network (CFAN), a private provider with expertise in developing innovative weather information tools in South Asia, to deliver hyper-local forecasts and related advisory to smallholders in India and Pakistan. CFAN’s products are anticipated to improve on existing forecasts in the region in terms of accuracy, lead times, and precision. Importantly, they can also be calibrated to predict weather phenomena that are particularly relevant for agricultural decision-making, such as prolonged dry spells suitable for fertilizer application or monsoon onset dates.
PxD’s pilot activities will test the efficacy of different intervention designs in both contexts, laying the groundwork for a large-scale randomized evaluation of weather forecasts’ effects on agricultural outcomes that we hope to implement in 2023. Some of the questions we will explore during this preliminary phase include:
- To what extent do farmers update their subjective weather expectations in response to different forecasts? What role do informational, cognitive, trust or other barriers play?
- How do farmers interpret probabilistic weather information? Do forecasts with a numeric probability of a weather event and a qualitative likelihood of the same event affect their beliefs differently?
- For which agricultural decisions could improved forecasts generate the greatest returns?
- Does improved weather information spread among farmers within and across villages?
The findings of the pilot and subsequent evaluation will have policy implications at PxD and beyond. For example, improved understanding of the returns to information products that reduce agricultural risk will inform PxD’s prioritization of new services, such as pest prediction models. The project will also seek to provide policymakers with evidence and incentives to increase funding to improve forecasts in regions that are under-served by governments and markets.
Brewing better weather services for Indian coffee farmers [link]
Coffee is a notoriously fickle crop. For example, heavy rainfall can damage crops, result in premature fruit-drop, increase the incidence of pests, and wash away fertilizer with negative implications for plant nutrient levels. Increasing weather variability and the incidence of extreme weather events associated with climate change will have significant negative effects on coffee producers. Given the sensitivity of the crop — and yields — to fluctuations in the weather, coffee farmers are likely to derive meaningful benefits from accurate and timely weather forecasts.
Insights from PxD’s Coffee Krishi Taranga (CKT), digital advisory service for small coffee farmers in India in partnerxhip with the Coffee Board of India, will be used to inform the design of a larger evaluation of the weather-integrated service and to scale an enhanced service to over 150,000 coffee farmers across four Indian states (Karnataka, Kerala, Tamil Nadu, and Andhra Pradesh).
More accurate information about medium-term rainfall — with a lead time of up to 15 days — will enable farmers to make informed decisions about applying nitrogen fertilizer and increase the likelihood that they apply this input during dry spells to reduce run-off and leaching. Similarly, if farmers are alerted to impending heavy rain, they can leverage this information to alter harvesting times or take other precautionary measures to protect crops and insulate yields.
In interviews conducted with coffee growers in August 2021, only 16% of respondents reported accessing forecasts. Integrating weather information into CKT’s existing services will broadcast weather forecasts to farmers tailored to their specific contexts and complement these forecasts with agronomist-designed advice.
Studies conducted in other contexts find that farmers form subjective expectations about upcoming weather events based on various factors, including their past experiences, local rules of thumb, existing forecast information, the costs and benefits of acquiring such information, and perceptions about how relevant weather-related risk is to their incomes. These expectations inform behavior over the course of the coffee crop cycle as farmers make decisions relating to input and investment choices, the timing of activities, and so on. The sum of these decisions, in turn, influences outcomes that farmers (as well as researchers and practitioners) are interested in – notably plant health, yields, costs, and profits. The goal of this research is to understand each of these elements through measurement and service pilots, A/B tests, qualitative interviews, and in-person workshops with farmers, agronomists, and extension agents.
The objective of the first set of interviews is to better understand how coffee farmers make decisions relating to the timing of agronomist-identified, weather-dependent coffee activities: fertilizer and lime application, coffee pruning, shade regulation, and harvesting. We hope to identify how weather fits into these decisions and what other factors influence the timing of these activities. If other limiting factors (such as the availability of an input) impact timing to a greater extent than the weather, forecast information with short lead times may not help farmers optimally time their practices without access to complementary inputs or information. These interviews will also help us identify how farmers interpret weather forecasts they already have access to, what impact incorrect forecasts have on their activities and on their trust in forecasts and the extent to which farmers discuss their expectations of upcoming weather with other members of their communities.
Coffee farmers in a subset of villages in our three study districts will then be invited to participate in in-person workshops, where they will interact with different forecasting formats. The workshop will be in the form of a ‘lab-in-the-field experiment’, where participants engage with an interactive platform that presents weather forecasts together with incentivized agricultural decision-making scenarios. Utilizing participants’ decisions on the platform, an ‘in-scenario’ weather ‘realization’ will be simulated, allowing participants to accrue a higher payoff for a ‘better’ decision. The best-performing forecast will accrue the highest cumulative payoff across participants and will inform our understanding of which forecast formats most effectively aid decision-making. The ‘best-performing’ customized-to-context weather forecast will then be piloted in the field among a sample of existing CKT users to evaluate whether it improves decision-making in a real-world setting.
Cottoning on: A free weather product for Punjab’s cotton belt [link]
PxD is building a free weather forecast product for farmers in Pakistan’s Punjab Province. The service will ultimately serve 490,000 smallholder farmers across the province.
To guide more informed product design decisions, in November the Pakistan team commenced with a set of end-user interviews with 55 cotton and wheat farmers. While 71% of wheat farmers cited access to weather forecasting information, only 45% of cotton farmers surveyed reported access to weather information. When asked if weather information “helped in planning”, 88 and 86% of cotton and wheat respondents respectively responded in the affirmative. Farmer users were asked to “Please list the types of weather challenges you have experienced” in the three years prior to being surveyed. Forty-three percent of respondents who reported experiencing weather-related challenges cited heavy rainfall and 30% reported high winds.
These types of weather incidents can be very costly for smallholder farmers with limited resources. Inundation washes away expensive inputs, creates mud that blocks sprouting crops, and creates conditions conducive to disease, while wind can destroy or severely damage crops throughout the cropping cycle. Further, when prompted to answer “Out of these weather challenges, which three have the largest impact on your crop costs and yields”, pest management was reported as the most common answer. Many pests and diseases thrive in particular weather conditions and can proliferate quickly if conditions are optimal. This suggests that a combination of advisory and weather forecasting information alerting farmers to be on the lookout for pests and to initiate pesticide application decisions sooner could be valuable as a means for reducing pest damage.
Another core challenge in developing this product is the quality of the weather forecasts themselves. The existing forecast services in Punjab tend not to be designed with the end user’s needs in mind. Three services in Punjab, Pakistan exemplify usability issues: the first requires user-initiated, user-paid inbound calls; the second requires internet access; and the third is only available to subscribers of a particular telecoms company.
Learnings from these research activities, coupled with insights generated by our collaboration with CFAN, will inform the final design of our Kharif weather forecasting product for cotton farmers. Once this product is up and running, the focus will shift to impact evaluation, including pick-up rates, behavior changes, and forecast accuracy. Pending a review of such impact outcomes, and funding considerations, the product will be further developed to cover the Rabi season and potentially scaled to additional countries.
This Kharif season, we are thrilled at the prospect of hundreds of thousands of Punjabi smallholder cotton farmers witnessing less fertilizer – and by implication, fewer resources – washed away or harvests inundated by unexpected rains, deploying more effective irrigation tailored to the forecast, and incurring fewer crop losses due to heavy rains and winds. When we asked our PxD Pakistan agronomist about the potential impact of this product, she said: “This product will reduce farming risks and expenses, and increase what is usually a farmer’s sole source of income – allowing for more investment into farming or money for personal expenses”.
CFAN’s precipitation forecasts
CFAN is providing a comprehensive range of forecast variables, including temperature, wind, soil moisture, humidity, cloudiness, and thunderstorms. However, the single most important forecast variable is precipitation. CFAN is using 15 day ensemble forecasts from both ECMWF and NOAA (GEFS).
CFAN’ provides probabilistic weather forecasts based on the forecast ensemble (e.g. 51 ensemble members from ECMWF for each forecast). The ensemble forecasts are transformed into meaningful probabilities through calibration of the forecasts by eliminating biases and distributional errors in the ensemble forecast (see schematic below)
Forecast calibration requires good observational data. Particularly in Pakistan, there is very little useful data from surface observations. For rainfall, we elected to use the satellite-based IMERG precipitation data, which does the best with extreme rainfall events.
While the ECMWF forecast system does quite well, our calibrations significantly increase forecast skill. CFAN provides a deterministic ‘best guess’ forecast Below are two forecasts for single locations in the Punjab
CFAN’s probabilistic rainfall forecasts are provided in terms of probability of exceedence of certain threshold daily rainfall amounts: 0.25 mm, 2 mm, 5 mm, 10 mm and 20 mm. Below is shown the value add from CFAN’s calibration for the threshold 0.25 mm, which is basically the rain-no rain threshold. CFAN’s calibration (red) provides substantial improvement to the skill relative to the native ECMWF forecasts (black), especially for shorter lead times.
The skill score used is the ROC, which is explained below
Our clients have also requested that we provide seasonal forecasts of monsoon rainfall. These forecasts help guide decisions on which crop varietals to plant, and how much to plant. CFAN is using a statistical-dynamical approach to provide a probabilistic seasonal forecast. While our approach shows skill, sometimes our forecast will be wrong. Our clients have asked how they can effectively make use of our seasonal forecasts, given that sometimes the forecast will be wrong.
Consider the following example from the chapter on Decision Making Under Deep Uncertainty in my forthcoming book:
Consider the following decision that farmers face annually. The farmer has a choice between two crops: Crop #1 produces a steady yield under all rainfall conditions, while Crop #2 provides large yield only under conditions of high seasonal rainfall. Since we can’t reliably predict rainfall on seasonal time scales, Crop #1 is the safest choice, although yield will be suboptimal if rainfall is high. Robustness becomes an important decision criterion when the consequences of making a wrong decision are high. If crop insurance is available to protect against potentially poor yields, or if sufficient savings are available, Crop #2 may be the best strategy. If these tools and resources are not available and the consequences of a few years of low yields would be disastrous, then robustness becomes the priority. An alternative strategy is to hedge by planting mostly Crop #1, but devoting a fraction of the land to Crop #2.
Over the season, each farmer faces a range of weather-related decisions over different timescales. While substantial confidence can be placed in CFAN’s rainfall forecast at 3 day lead times, confidence in the forecasts decreases with increasing lead time, with relatively low confidence in the seasonal forecast. Hedging strategies are well matched to probabilistic weather forecasts. We will be working with the PxD team to consider how hedging strategies could be implemented in response to the probabilistic weather/climate forecasts.
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About the size of state of NY with 8X the population… obvious need of any support us coffee-drinkers can give…
The donor for the Karnataka piece is the Walmart Foundation
It is good to see climate science and weather forecast helping those most in need. I wish you the best with those projects.
The monsoon depends basically on land/sea surface temperature contrast. I guess monsoon predictions must have been improving with data improvements. There is a huge part of the world population whose livelihood depends on the monsoons.
I think it was your piece, the article ‘Nature Unbound IX – 21st Century Climate Change’. Fig 122 the red Eddy curve was an illumination. Further ferreting showed all peaks and roots of that curve as far back as 6k2bce were times of great change, recorded historically. We are approaching one such peak.
Four years on. see link here (bottom update pic) https://melitamegalithic.wordpress.com/2019/03/15/searching-evidence-update-2/
REI’s dragon-kings visit at such times, some benign (have not looked at details) some quite malevolent. Recent trawling brought one peak in focus. In the RWP, precisely year 173CE the Chinese astronomers recorded outlier numbers for earth axial tilt (possibly/probably within 24hrs of each other; a small increase). That abrupt change showed up (in a paper) in elevated glacier melt and lake sediment.
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I was not aware you have a forthcoming book, that’s very exciting, particularly if it gathering together your thoughts on uncertainty. Is there any more info available?
Excellent, thanks. I work at a natural resource management agency in Canada. I plan to pre-order a couple copies to circulate with colleagues. This type of book is sorely needed.
“interesting”, like “nice” is often received as faint praise, hence condemnatory. But this piece is really interesting, and I shared it, which I seldom do.
Judith Curry, thank you for this essay and for the work of your company.
Many of these farmers may have limited access to power, limited education, and limited access ( or understanding) of information technologies. Hard to see how these hurdles are overcome in a practical sense.
They all have cell phones
Even in US, cell phone coverage is sporadic in many rural areas. Tribal areas, say in Pakistan, pretty spotty. Even with a cell phone, does not follow farmers can necessarily employ somewhat esoteric information.
Have seen many a whiz-bang technological advancement fall flat on its face because not particularly compatible with folks presumably targeted for the advancement. Needs to be understandable to be useful.
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Judith, didn’t I hear someone sometime ago criticizing you for having corporate clients, i.e. insurance companies, etc? Obviously, criticism like that, somehow meant as horrifying, is idiotic. But I wonder what their one dimensional little brains would say about this? Actually, I really don’t want to know, and I’m sure you don’t either. Nice gig, though. Good luck!
Oh yes, i am criticized for not being on the government dole, but rather being paid for something that companies (including energy companies) and governments actually find useful
Some people have an innate understanding of risk. There are people we call frugal, and most people who live below a certain monetary threshold, both have an understanding of the value of their resources and time, and what it means to lose them. However, the greatest understanding of risk comes from having your own business. For now it’s not just that you are risking everything, but you must convince others to purchase your products/services which is asking them to depend on you, in most cases, to reduce their risk. You are contracted to perform and accountability is swift, either positive or negative. To continue, or as they say, to be a going concern, it requires a continual high level of effort to succeed. This effort is sustained by self-interest. Self-interest isn’t just money, although it is a fundamental part. Sometimes it can be as simple as ‘proving it’ to oneself, or others. But it can’t be limited or it seems to falter. Without risk, no matter the exact motivation(s), the fire to the feet is missing. This understanding of risk is completely lost on the left, and those who live by the sweat of others, i.e. children of parents or children of the state.
I figure it’s likely those criticisms come from people who do government-research-grants for a living. An activity that frequently results in nothing especially useful and in some cases results in outcomes that are detrimental to life as we know it.
Well done, Judith, a very worthy project. Re Mike Keller’s comments, when I was last in India (2017), cell phones were cheap and ubiquitous.
I don’t know about Pakistan, but cell phone coverage is better in India and Bangladesh than in many parts of the U.S.
I have a comment in moderation … was just saying that your project in South Asian sounds great and the best of luck with it!
Lots of mentions of climate change, acknowledging the potential problems to come. Good to see that recognition here and on the website of PxD. Together I hope you are able to help the farmers face the coming climate change problems with hope and, hopefully, success.
The weather has always been a variable risk for farmers. Nothing new here.
From the article above:
“Because weather variability can be disastrous for poor households and is increasing with climate change…”
That’s new, isn’t it?
No, JMurphy, the climate has been changing pretty much since the Earth formed.
Did you miss the description of the current problems: increasing. Not easy to adapt to, unfortunately, as many species, not just us humans, are discovering.
We’ll, at least Dr Curry is trying to do something to help people cope with that increasing variability.
Well, JMurphy, the overarching question is to what degree are fossil fuels responsible for global warming? Dr. Curry has stated in the past she believes part of it is due to fossil fuels. But to what degree hasn’t been nailed down. The fact that global temperature has been down for the last several years whilst CO2 is still going up does not lead credibility to the “fossil fuels bad” hypothesis.
An equally important point to be considered in relation to the agriculture/fossil fuel question is that agriculture is now fully dependent on fossil fuels.
Mechanised agriculture is not so old, and great changes occurred within the last century with cheap IC engined tools. Now the change is total. Before it was primarily the horse/ox that powered the plough. Those animals needed no large industrialisation to support them. They ate from what they themselves produced and returned it as fertiliser.
Not so the internal combustion engine.
melitamegalithic – well, imagine what infrastructure would have to be in place to replace tractors with horses. Lot’s of land would have to be dedicated to feeding the horses. Then there’s the fact that one tractor does the work of hundreds of horses. Also, tractor produce CO2, plant food, so they too return goodness to the plants.
Beyond that, imagine what size battery would be needed to run the large tractors. That’s not pretty.
And then there’s the fact that one form of fertilizer is made from natural gas.
NITROGEN (N)For nitrogen-based fertilizers, the largest product group, the process starts by mixing nitrogen from the air with hydrogen from natural gas at high temperature and pressure to create ammonia. Approximately 60% of the natural gas is used as raw material, with the remainder employed to power the synthesis process. The ammonia is used to make nitric acid, with which it is then mixed to produce nitrate fertilizers such as ammonium nitrate (AN). Ammonia may also be mixed with liquid carbon dioxide to create urea. Both these products can be further mixed together with water to form UAN (urea ammonium nitrate) solution. .
In my view the matter is much bigger than it seems; and it needs a holistic approach. Two major problems I see at a glance.
a) mechanising to a cleaner form of prime mover (eg. hydrogen powered IC engines) but which needs a complete restructure of the industrial process.
b) long cycle adverse climatic change that is independent of any gases, that may render large regions unproductive for long decades.
My main concern would be ‘It is not how to help the small farmer, it is how to feed the large cities’.
m – If you want to ensure a reliable food supply, the last thing you want to do is implement an unreliable and more expensive energy supply.
Quite so. But.
One may need to; for various reasons. Health, fuel availability (do we have a preview), –. It was difficult replacing the horse with the IC engine.
“Increasing weather variability and the incidence of extreme weather events associated with climate change…”
I’d be looking at the discrete solar forcing of NAM anomalies causing climate change, through ENSO, the AMO, and the IOD. And then predict the NAM anomalies.
Oil and gas have some 40 years of economic reserves left at current production rates. To ensure future energy supplies alternative sources are required at a massive scale.
Robert, known reserves have always had a similar or lower figure because there was no reason to explore when you had 40 years of reserves.
So we don’t know where coal and gas reserves are?
Comparing reanalysis to climate models suggests that decadal variability is much as 0.3 degrees over 40 years. So about half the warming of the past 40 years was natural. The physical mechanisms can be inferred from
satellite observations. In a system where small changes trigger large climate shifts AGW could be a problem.
Robert I. Ellison. ” In a system where small changes trigger large climate shifts AGW could be a problem.”
Not a very helpful comment,
but small changes could trigger large shifts in perception though a fixed mindset could be a problem for a system using logic.
“A small forcing can cause a small [climate] change or a huge one.”
— National Academy of Sciences, 2002.
Unless you have data that falsifies the hypothesis – you have an opinion. That and $5 will get you a\ coffee.
We have the historical evidence that change occurs cyclically -a 980yr cycle, with or without AGW. (inflection points half way, ~490yrs). How big the shift were in the past we can get an idea from archaeology, form evidence of civilisation collapse. What needs to be firmly determined is how abrupt is the causation.
The 3200bce is well known, the following 4k2 event is still quite fresh; as is the ~1250bce Aegean collapse. Nearer is the 536ce, the ” https://www.science.org/content/article/why-536-was-worst-year-be-alive “. All Eddy cycle roots.
Peaks also times of change. 173ce (see above posts) gave an indication of what the slight change can be. We are heading towards one ( maybe at about the time REI says oil and gas will run out).
Long term plans may take as long as half a century. (a power-plant life cycle is 30yrs min in specs, more for nukes).
Robert – a hypothesis IS an opinion!
“A small forcing can cause a small [climate] change or a huge one.”
How would you falsify this piece of wisdom?
A hypothesis is testable.
Please answer the question, or shut up.
Try not to be clueless and offensive George.
Yes, I am clueless. You asked us for data that would falsify the “hypothesis”. and you doubled down writing that is “testable”. Please use your superior wisdom to propose how to test it.
It has been known to science for decades.
How does this falsify “A small forcing can cause a small [climate] change or a huge one.”? You call this a hypothesis? You have a fixed mindset.
It is where the NAS made that claim and backed it up with evidence.
To add something to the above, and in relation to link “Climatic change and ancient civilizations”; and also found here https://whc.unesco.org/en/list/1592/
Quote “Liangzhu culture in eastern China, which thrived in the Yangtze Delta from c.5,300 to 4,300 years ago” — “But c.4,300 years ago, the ancient city was suddenly abandoned and the Liangzhu culture collapsed.”
Two points of note: 1) the abrupt collapse, and 2) the date at 2350bce. The date is one that turned up early from tree-ring studies (with good precision). The domino effect would become known as the 4k2 event.
Your first link was on volcanic cooling. And cultures all over the world have come and gone for many reasons.
When I use term abrupt climate change it is shorthand for nonlinear internal responses to change in control variables.
Volcanic coolings do not stick to Eddy cycle roots. Same with civilisation collapse world-wide.
Neither do they -volcanic coolings- leave a change in solstice sunrise points on the horison.
As to control variables the best clue is that that the ancients had unraveled centuries ago – namely planetary.
See link: https://iopscience.iop.org/article/10.3847/1538-3881/ab5365 in conclusion “– with some stable solar system variants featuring
oscillations in Earth’s orbital inclination that approached, or even
exceeded, ten degrees.” There is evidence of repeated change, all very abrupt. Science has been revering the wrong dogma.
Please note typo in earlier post, which should read “Not Same with civilisation collapse world-wide.”.
Major civilisation collapse occur around Eddy cycle roots. As in link (indicated in bottom pic, all checked out) https://melitamegalithic.wordpress.com/2019/03/15/searching-evidence-update-2/
Planetary orbits in the solar system are mostly smooth and predictable and sometimes intensely chaotic. But I ask myself what is at the root of cycles.
REI ” But I ask myself what is at the root of cycles.”
What is becoming evident is a dynamic disturbance of earth’s orientation due to planetary alignments. From the various dates it appears to be around Eddy roots; and peaks.
The root at 2345bce was a disturbance hypothesised by GF Dodwell in 1936, but was ignored by science. It was a quite large disturb from a ~14deg tilt with respect to sun, to near today’s. Identified in a realignment of megalithic calendar solstice to equinox angle change – which is measureable. An earlier change occurred at 3550bce, a peak, resulting in the abrupt desiccation of the Sahara.
That all remained for the past six years as questionable. Looking back at the old measurements of earth tilt, year 173CE was a disturbance caught by the Chinese while trying to measure tilt. Delving deeper there were changes, major tsunamis, and the beginning of the end for the Roman empire. It was then all ‘downhill’ to year 536ce and the mediaeval dark age, the ‘dark age cold period’.
The disturbance was small, and from the old measurement eventually returned to earlier trend, except that it left a major disturbance in glacier melt and lake sedimentation (see Arctic Holocene glacier fluctuations reconstructed from lake sediments at Mitrahalvøya, Spitsbergen; see fig6). The details here correlate. Even the disturbance at 2345bce is recorded, so GF Dodwell was on the right track all along.
Quote from REI’s link page “From apparent disorder, order emerges.” Its the opposite : “From planetary order emerges climate disorder”.
Probable estimates of rainfall are not always that happy.
We can have a 20% chance of rain in Shepparton daily for a week which in reality means no rain for the week.
We have warnings 6-8 times a year of possible > 90% heavy rainfall [an inch ] in 5-7 days but only 1 of those predictions of a medium time span come true.
Whether he wants to or not the farmer has to plant his crops or with coffee trees prepare them for as good a harvest as possible.
We have apples and pears here in a dry climate made possible by irrigation.
Weather forecasts are more appropriate for pest control, spraying, which is better done when not raining so those forecasts could help.
Massive or communal farms might benefit better than small crop owners.
I am carping a bit. Any knowledge is helpful of course.
You deserve success from this work.
It is strange to see criticism for using business structures that were dominant 30 years ago. Strange that you should have set up a charity and tied your progress to bureaucratic rules???
You have long displayed a resilience and oodles of ability from which others should learn. We Aussies might need your assistance, given the way our national ignorance is increasing under political encouragement that is now leading to food shortages. $15 for an Iceberg lettuce. (Sorry, I seldom write of politics).
Do keep us informed of progress. Geoff S
$6.20 for an imported iceberg lettuce. I suggest that Geoff consume produce seasonally and avoid shopping at convenience stores.
Here’s a guide.
US$4.28 for a head of lettuce? US$1.49 here in Arizona … and that’s high!
Winter supplies from Queensland were hard hit by La Nina floods. Flying it in is costly these days.
Obfuscating again? Your Coles lettuce price was higher 2-3 days before. Today I bought fresh green beans at $28.50 a kilo. They are good quality, they taste beautiful like they were grown properly, not that fly-specked ‘organic’ rubbish.
I am about to write to their ABC to ask if they will now cease their promotion by program comments and magazine, of this organic thing. I intend to cite balance and evidence noting the killing field farms of Sri Lanka, where farmers were ordered to go organic and to reduce synthetic fertilizer demand.
I feel for Judith, doing her best to help to feed people in developing countries, while there is this critical situation not so far away in Sri Lanka where scientific ignorance has been so deadly. Geoff S
Another irrational rant this time blaming food prices in Coles on organic farming and dragging human rights abuses in Sri Lanka into the mix.
Green beans are available year round – but the main season is between May and October. Quality costs extra. Asparagus is good at the moment. The upside is that farmers are doing great. Good soil moisture and great prices. Next they will be complaining about doing it tough.
I am all in on anything that makes farming less stressful and more profitable.
My company Climate Forecast Applications Network is a for-profit company that pays taxes. Most of my clients are other businesses, this particular client is a not for profit. Note: all of my projects for businesses are confidential and I can’t write about them
‘ACCESS-S outlooks are based on a 99-member ensemble. This is a common climate forecasting technique where the model is run 99 times with slightly different initial conditions to capture a range of likely future scenarios.
Being a dynamical model, ACCESS–S is undergoing continuous research and development. Advances in the science of seasonal prediction, improvements in the observations and how they are fed into the model, as well as increases in supercomputing power are just some of the ways the model’s accuracy will increase over time.” http://www.bom.gov.au/climate/ahead/about/model/access.shtml
The Australian model has commonalities with the UKMO model and thus with ECMRWF. A big link in the latter is Tim Palmer -who claims to have won the battle for probabilistic forecasting over 30 years. There is below a new YouTube video. Time Palmer is a giant who has posited that the underlying principle of the cosmos is fractal entropy. It’s Russian dolls all the way – billions of them in the Earth system unpacked from Navier-Stokes. Numerical weather forecasting has come a long way. It is now seamlessly integrating into initialized seasonal to decadal scale modelling. These models take inputs for all the diverse monitoring systems. Without a doubt ultimately in the metaverse with machine learning and artificial intelligence.
One thing puzzles me – your calibration. Looking at your top graphic – that seems to be adapted from Julia Slingo and Tim Palmer (2012) – it shows small differences in initial conditions propagating as divergent uncertainty over the time of the simulation. It is the temporal chaos of nonlinear equations. Distinct from the spatiotemporal chaos of evolving fractal patterns of ocean and atmospheric flow. Flow patterns do shift – it’s hydraulics 101 – giving rise to the ersatz oscillations of major climate indices with feedbacks in ice, dust, biology, upwelling and downwelling, cloud, temperature, humidity…
As model trajectories evolve in time the calibration can’t be linear or a constant. If it is calibrated to a mean then all information on probabilities is lost. Perhaps you would be so kind as to expand a little on your procedure?
Ersatz climate oscillations are useful in interpreting weather forecasts and understanding climate processes . A picture of broad periodicities can be formed. Ocean and atmospheric states can be corelated to observed precipitation – or inferred by geomorphology. Pick a few that are close to the current state and estimate probabilities. Sir Gilbert Walker was the first to recognise the Pacific Ocean influence on the Indian monsoon 100 years ago. Influences in the Australian region are:
There is a great need for this work. If Walmart is socially conscious enough to pay for it – you are on a winner.
I caught a snippet here of a discussion on Elon Musk and artificial intelligence. The metaverse is being built on server farms around the planet. There is an open source Kubernete environment with virtual computers called containers whizzing about. It is scalable – which means it can just keep getting bigger. Full of artificially intelligent banking, insurance, science, marketing, teaching, gaming… It’s an exciting time to be alive – but for God’s sake keep weapons out of the metaverse.
BOM has a well considered agricultural service.
Quick reply to RIE’s comment above. Our calibration scheme uses an ensemble-based approach, we calibrate the distribution of the ensemble. Note: Tim Palmer is retiring from Oxford and ECMWF at the end of the year. He is a close personal friend of mine and Peter’s. Peter is invited to give a presentation at Tim’s retirement symposium in Dec, which will include this work.
I’ve spoken to Tim once. A perfect gentleman. I had noted his retirement celebrations. His career has been stellar and his trajectory fractal. I trust that he is far from finished.
I’ve spoken to Tim once. A perfect gentleman.
The trajectories of the 99 members of an ensemble, for instance, define a probability density function. A single member has a trajectory that can’t be predicted. The model as a whole can be tuned to real world outcomes. Which is where the next forecast starts. One can work backwards to determine how close the model is to reality at different times. But perhaps not so useful when projecting forward with nonlinear equations. I’ll await the details.
Tim has a book in press “Primacy of Doubt” and will start working on another one
Involvements with small farmers that result in positive gains in my view are going to depend on the current political regimes these farmers operate under and the willingness of those regimes to accommodate changes.
I would think that farmers who have for some generations worked their land would have lots of nonexpert knowledge that would need to be taken to account before deciding what innovations could and would be used to the farmers advantage. These efforts and involvements by outside entities would appear to me to be daunting.
This has always been one of the major problems for people who want “to save the world!!!” You can ship all kinds of money and materials to some countries and it simply ends up enriching the person or people in power. It’s a loser’s game to try to help them in that manner. And I believe the US has proven along with some allies that the military solution isn’t one unless you are ruthless and all in.
I apologize fully if I have commented here in any way detrimental to you. We comment based on what we interpret and usually, we do not know the story in enough detail.
Your help to countries with farming that can be improved with injection of more knowledge has to be supported as a first approach, so I would expect nothing but enthusiasm for your efforts.
Are there specific problems that you have run into that might benefit from airing them here? Your numerous past blogs over the years have earned you respect that some of us like to reward, if we are capable and relevant.
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I am a resident of South India any about everyone here has a cell phone. Coverage is usually ok.
How can I return to making comments on your website? I was registered with WordPress, but they now require that I establish a website.
Judith Curry Twitter: V. interesting new paper: “which project further weakening of North Atlantic Oscillation, North Atlantic cooling an…
I don’t do Twitter, but agree. The stratosphere-troposphere coupling is the biggest atmospheric surprise in decades. Once meridional transport increased at the 1997 big climatic shift climate initiated a different regime that does not favor surface warming, and the coming AMO decrease is just the central phase of this regime that could last until the early 30s. This is part of what Marcia Wyatt and Judith Curry studied as the “Stadium wave,” and I discuss about its causes in my coming book offering some interesting new evidence.
I thought the science was settled.
Interesting to compare this to Sri Lanka with no or limited fertiliser. It didn’t go well.