Report Issued Today Examines Improving Long-Term Climate Forecasts
NAS report: “There is an apparent plateau in our ability to make accurate seasonal forecasts”
Response to article Posted on WUWT ;
Report Issued Today Examines Improving Long-Term Climate Forecasts
MIAMI — September 8, 2010 — Operational forecasting centers produce climate predictions that provide input for important decisions regarding water management, agriculture, and energy. “Assessment of Intraseasonal to Interannual Climate Prediction and Predictability”, a new report from the National Research Council/National Academy of Sciences, examines current capabilities for making climate predictions — such as seasonal hurricane or longer-term drought forecasts — and identifies opportunities for improvement.
Below are excerpts from the “Assessment of Intraseasonal to Interannual Climate Prediction and Predictability” report PDF with my related comments for their consideration.
“The predictions produced by climate models are inherently probabilistic and have considerably lower skill than 1-2 day weather forecasts. They are usually of little use in planning everyday activities. However, climate predictions are very useful to government agencies, non-governmental organizations, and private companies for policy and longer-term planning purposes. Examples of applications include drought mitigation, malaria prevention, farming, pricing of insurance, and managing energy resources.”
Very telling eh?
“The second type of source of predictability is related to patterns of variability or feed backs. Coupling among processes in the climate system can give rise to characteristic patterns that explain some portion of the spatial and temporal variance exhibited by key climate variables, such as temperature or precipitation.”
However they seem to leave out the effects of the interactions of the Earth Moon system, as if the sun was just the overhead light that glares on their monitors, and the moon was not to be considered at all, due to past superstitions about allowing “astrology” into the sciences.
Systematic blocking of all grant fund applications for the study of Lunar cycles, since about 1950, has suppressed the study of the real tidal forces, and the various orbital resonance effects of the moon on the weather. Hence the parts that they are missing in solving the weather puzzle are almost all answered by looking at the timing of the effects of the moon on the weather.
Many of the cyclic periods, that they have a full pocket of acronyms for, are derived from the orbital dynamics of the Sun, moon, and planetary interactions affecting dynamic ocean / atmosphere oscillations, below I have pulled sections of their paper with comments and advice I could give them to fix the problems they are having.
“Although the committee agrees that these goals should be pursued with the intent that they contribute to operational ISI forecasts, the initial efforts to investigate these unexploited sources of predictability fall largely on research scientists.”
Sorry they have not allowed any funding for the needed research? No they needed it to fund their model building campaign, which by still avoiding looking at other “natural climate variability” factors, have come to an end of their progress, out about 10 to 15 days into the future. To my mind that means there is something with a period of about 7 to 10 days that they are not considering.
Most of the research that was funded prior to 1950 studied the Lunar light phases, moon rise and set times effects on precipitation periods of the day, and were usually composites of 28 day phase cycle relationships for entire years of data records, sometimes separated by seasons, but usually clumped together to negate any chance of finding composite modulated cyclic pattern signals.
“Specific activities include holding workshops focused on specific areas of model and forecast development, encouraging scientists that work at operational centers to participate in scientific meetings focused on modeling and the use of observations, granting of short term positions in operational centers to academic researchers, and improving the speed and manner by which new data sets generated by operational centers are shared with the broader research community.”
You will notice they still cling to the good old boy method of only assisting or listening to those in ” positions in operational centers to academic researchers” and the data is only freely shared by members on the team “data sets generated by operational centers are shared with the broader research community” all others are required to pay for any data they may require, and public access is strictly forbidden to even the knowledge, of the existence of most of the recent data sets resulting fro NASA’s many interplanetary satellite missions.
“(2) Operational ISI forecasting centers should establish public archives of all data used in forecasts including observations, model code, hindcasts, analyses, forecasts, re-analyses, reforecasts, verifications, and official forecast outlooks.”
Well it is about time, how about making the access to historic data sets available to the general public to conduct their own research, like is done in Astronomy and close working relationship between the amateur observers who do most of the raw observing to find new objects, that free up the pros to use the big scopes for in depth research, verification and follow up details.
“Historically, linear statistical analyses of observational data have provided an awareness of many patterns of variability that have been useful for making ISI forecasts. Recent research demonstrates that nonlinear methods can yield statistically significant increases in prediction skill on ISI time scales when compared to traditional linear techniques. However, these techniques have not been incorporated operationally. Therefore, nonlinear alternatives should be explored to augment our current knowledge.”
Too bad they did not try to tease the cyclic nature out of the patterns, nor tie them to the driving forces that determine their periods, now that computers are available to do the work easily. I have done most of the background work needed to incorporate the cyclic natural patterns of variability, into the basic process used in their stand alone models, that would keep them based in reality and not just flap around like butterflies two weeks, or even two years into the future.
“The physical processes underlying ISI variability are often poorly understood.’
“Work should be carried out to move toward more complete inclusion of climate processes in the models.”
This site is a compilation of the process, and a beta product of the additions they need to make to the models to stop the deterioration due to runaway feedbacks of the unrealistic limitations they have imposed by leaving out Lunar atmospheric tidal influences especially the 18.6 year and 27.32 day declinational component. The forecast presented here extended out 72 months into the future when first posted on line in December of 2007, are but the composites of the past three analog cyclic patterns given as a daily forecast for this cycle. I have been watching this basic cyclic pattern work for over 20 years now, with consistent above average performance, that beats the results the models put out in the 5 to 7 days range.
“These types of day-to-day and season-to-season variability, caused by strong, regular, and periodic external forcing from the sun can be accurately predicted.
But beyond these daily and seasonal cycles, the dynamics of the climate system are more complex and incompletely understood, challenging our efforts to make predictions. For example, to answer a question like “Will the upcoming winter be colder or wetter than usual?” requires an understanding of climate variability on the timescales of weeks, months, and years. This variability stems from the atmosphere, the ocean, the land, and the coupling between them.”
With out the addition of the understanding of how the lunar atmospheric tides, that is “the coupling between them” drive the weather and climate variability in these time scales of weeks, months, and years, they are still lost.
“The ability of the atmosphere, ocean, and land to interact and affect one another occurs over a broad range of spatial scales and timescales. These interactions give rise to complex, often nonlinear, dynamics making it difficult to understand and predict the climate variability that we observe. While much progress has been made extending weather forecast skill to a week or more, the ability to make predictions on timescales longer than two weeks is still limited. At shorter timescales, most of the important dynamics reside within the atmosphere. But for longer timescales, the storage of heat and moisture by the ocean and the land becomes more important. Unfortunately, we have less information about the ocean and the land than we have about the atmosphere, and we often lack a full understanding of the interactions among the three.”
They openly admit the lack of understanding from limiting the admission of cyclic pattern drivers into the models, but refuse to pick their head off of the table, and look up to the sky to see the moon, and recognize its effects, as the missing pieces of the puzzle.
“::3. Identify any key deficiencies and gaps remaining in our understanding of climate predictability on intraseasonal to interannual timescales, and recommend research priorities to address these gaps;”
That is what I am doing here presenting the research I have done over the past 30 years. I could be available on short notice to make recommendations based on my acquired knowledge, and suggest lines of inquire that will be most effective at increasing lead time for accurate forecasting. If you question my results, a simple skill score verification of the basic beta forecasts posted on this site should clear the air.
With active programing and the additions of algorithms for the influences of the outer planets to the basic premise used herein much could be gained. I only seek to assist in the expansion of the understanding of the process they are struggling with in their own created dark.
“For instance, many factors external to the atmosphere were ignored, such as
incoming solar radiation and the state of the ocean, land, and cryosphere. Single events, such as a volcanic eruption, that might influence predictability were not considered; nor were long-term trends in the climate system, such as global warming. In addition, the models were unable to replicate many features internal to the atmosphere, including tropical cyclones, the Quasi- Biennial Oscillation (QBO), the Madden Julian Oscillation (MJO), atmospheric tides, and low
frequency atmospheric patterns of variability like the Arctic and Antarctic Oscillations. These additional features are important for the impacts that they may have on the estimates of weather predictability, as well as for their influence on predictability on longer climate timescales.”
I think that by looking a the effects of the outer planet interactions with the sun as the Earth runs around her orbit will be most illuminating, in finding these cyclic patterns and their interactions.
“The second category involves patterns of variability—not variables describing the state of the climate and their underlying inertia, but rather interactions (e.g., feedbacks) between variables in coupled systems. These modes of variability are typically composed of amplification and decay mechanisms that result in dynamically growing and receding (and in some cases oscillating) patterns with definable and predictable characteristics and lifetimes. With modes of variability, predictability does not result from the decay of an initial anomaly associated with fluxes into and out of a reservoir, as in the first category, but rather with the prediction of the next stage(s) in the life cycle of the dynamic mode based on its current state and the equations or empirical relationships that determine its subsequent evolution.”
That is what I have been saying for at least 20 years now!
““Teleconnections” is a term used to describe certain patterns of variability, especially when they act over relatively large geographic distances. Teleconnections illustrate how interaction among the atmosphere, ocean, and land surface can “transmit” predictability in one region to another remote region.”
The lunar declinational atmospheric tides are global in nature, why would they not be connected between regions?
“The third category involves the response of climatic variables to external forcing, and it includes some obvious examples. Naturally, many Earth system variables respond in very predictable ways to diurnal and annual cycles of solar forcing and even to the much longer cycles associated with orbital variations.”
You just need to include the moon and the other planets to get the whole picture right enough that it resembles reality.
“Patterns of Variability
Different components of the climate system, each with their own inertial memory,
interact with each other in complex ways. The dynamics of the feedbacks and interactions can lead to the development of predictable modes, or patterns, of variability. It should be noted that the descriptions for the patterns of variability provided in the following subsections describe their “typical” behavior, focusing on commonalities among observed events and the mechanisms that drive the phenomena. In reality, the manifestation or impact of a pattern may differ from these “typical” cases since the various patterns of variability can be affected by one another as well as by the unpredictable “noise” inherent to the climate system, especially in the atmosphere.”
The use of analogs with appropriate long term periods of cyclic return can filter out a lot of the noise, by bringing the short term cycles into focus in phase again so the combination clears out the noise, just like multipath and polarity radio reception in tropospheric scatter application, I used to maintain in the 7o’s, can be recombined at the baseband level to reduce noise levels when reconstructed.
“Additionally, the Quasi-Biennial Oscillation (QBO) of the stratospheric circulation offers a source of predictability for the tropospheric climate. The stratospheric QBO in the tropics arises from the interaction of the stratospheric mean flow with eddy fluxes of momentum carried upward by Rossby and gravity waves that are excited by tropical convection. The result is an oscillation in the stratospheric zonal winds having a period of about 26 months. While our weather and climate models do not often resolve or represent the QBO well, it is one of the more predictable features in the atmosphere, and it has been found to exhibit a signature in surface climate (Thompson et al., 2002).”
The average QBO period is about the same as two Jupiter earth synod conjunctions or the number of days it takes for the slewing of the relationship between lunar phase and declination to slip back into sync. So why not just add the lunar influence back into the mix, and make a whole cake that works. The following speaks for itself, I do not disagree with any of it, but I would be more than glad to assist anyone who really wants to find the answers that are still lacking, with whatever assistance and understanding I can offer.
“Gaps in Our Knowledge
Our understanding of ISI climate predictability—both of its sources and extent—is still far from complete. Numerous gaps still exist in our observations of climate processes and variability, in our inclusion of the wide range of relevant processes in models, and in our knowledge of the sources of predictability:
- We cannot yet claim to have identified all of the reservoirs, linkages, and teleconnection patterns associated with predictability in the Earth system. For many of the predictability sources we have identified, we cannot claim to understand fully the mechanisms that underlie them. The observational record contains many non-stationary trends that may relate to predictability but are not yet adequately explained. The science is proceeding but is encumbered by the overall complexity of the system.
- The models that have been used to evaluate the known sources of predictability and to make forecasts are known to be deficient in many ways. Many key processes associated with predictability occur at spatial scales that cannot be resolved by current models. Examples in the atmosphere include cumulus convection, boundary-layer turbulence, and cloud-aerosol microphysics; examples in the ocean include horizontal transports associated with eddies and vertical mixing. In addition, processes associated with the coupling of the ocean or the land surface to the atmosphere through the exchanges of heat, fresh water, and other constituents can be difficult to resolve. The models thus rely on parameterizations, which are simple approximations that often have to be “tuned”, making them undependable in untested situations. A wealth of literature is available on the deficiencies of current, state-of-the-art climate models; it indicates that currently available dynamical models do not always outperform simple empirical models or persistence metrics.
- Even if the models used were perfect—even if they included and represented accurately all physical and dynamical processes relevant to predictability at adequate spatial and temporal resolution—they would still be limited in their ability to make accurate forecasts by deficiencies in our ability to initialize prognostic fields. The degree to which these gaps limit our ability to make forecasts—or, stated another way, the improvement we could make in forecasts if these gaps were fully addressed—is difficult to ascertain. Exploring sources of predictability, in particular addressing gaps in our understanding of these sources, might yield substantial improvements in forecast performance. Here we briefly outline several sources of predictability for which gaps in understanding can be clearly delineated.”