2023 Climate Review for One Nation / archived / read-only

 
  • Part of: Points on Climate Change
  • Scope of the review
    • How does the CO2 data from the CSIRO Cape Grim station compare to other data?

      • CO2 stations in the Pacific

    • Is there other CO2 data showing fast term local changes?
      • The OCO2 and OCO3 satellite projects have global gridded daily and hourly data but as of 2023-05-15 I have not been able to successfully logon. Have reported the difficulties but no success.
      • Orbiting Carbon Observatory-3: OCO-3 Data Center
    • Review the latest surface temperature data from the Australian Bureau of Meteorology (ACORN SAT)
    • Review the latest surface temperature data from the Global Historical Climate Network (GHCH V4)
    • Review the latest surface temperature data from the Hadley/CRU (HadCRUT5)
    • Review the satellite temperature from UAH.
    • And lots more....
    • Could you please provide past examples of far higher warming than what we have experienced since 1995 when China, India, Russia, Brazil, USA, Europe produced record quantities of CO2 yet temperatures are flat or essentially flat for 28 years
      • CH,IN,RS,BR,US,FR,GM
    • Malcolm - And for each of the above, the raw data and the data with El Nino and La Nina removed. You’ve produced this in the past.I can't remember anything about this. Any clues?
  • Status of the review
    • 2023-05-26 Have responded to some of Malcolm's requests
    • 2023-05-26 Mostly just the graphs for later commentary and context.
    • 2023-05-26 Still to add graphs on the adjustments made to the raw data by various organisations.
  • Climate is the 30 year average of weather. - Today's weather has a negligible effect on climate.
    • Climate is the 30 year average of weather but is utterly more complicated than the average of the current daily measurements of temperature, humidity, rainfall, cloud cover, wind speed and direction.
    • Climates with the same average value of temperature will differ in the occurrence of long hot periods or long cold periods. Similarly for rainfall, wind etc.
    • At a particular place, it is reasonable to ask if the current weather is unusual for the climate.
    • The relevance of the climate of one place to the weather at another place will depend on the distance between.
    • If climate is determined over a region, its predictive relevance to the weather at any particular location will be reduced. The relevance of the global climate or any of its statistics to the weather (or predicted weather) at a particular location is near zero.
    • Any decision based on "climate" should be made at the lowest possible level as the confidence limits on any more regional "climate" expand so quickly as to be useless.
    • The satellite data is global but exists only since 1980. The two organisations that process the satellite data (UAH and RSS) do not agree even on the Atmospheric Layers to use.
    • Thermometer data exists from about 1800 but the long running series are mostly in Europe or the USA. The coverage of ocean temperatures is worse.
    • Thermometers made before 1900 used an unsuitable type of glass which was subject to Joule Drift which cannot be corrected. They cannot be used to measure a rate of change of 1°C per century.
    • Other quantities may be measured in present time that may be used to infer temperatures in the past but these can not (or at least are not) being applied regularly across the land and oceans. The Arctic ice core temperatures correlate to some extent but understandably have local differences. The Antarctic ones are similar with significant differences from the Arctic.
    • Knowledge of some globally averaged proxy data is of little relevance to any current local policy maker.
  • Major Cycles or Events in the Climate - Overpower short term trends.
    • There are cycles in the weather. The weather varies in a strong daily cycle. The range of weather varies strongly in a yearly cycle.
    • The amplitude of the daily and yearly cycles of weather must be included in a comprehensive statement of the climate of a place.
    • If there are cycles in the weather longer than 30 years then even a comprehensive statement of the 30 year climate of a place has only limited predictive usefulness.
    • In the written history there have been at least 3 cycles of warming occurring about every 1,000 years. Each one has been weaker than the previous. Minoan Warm period about 1500BC, Roman Warm period 250BC to 400, Medieval Warm period 950 to 1250, Current Warm period from 1850
      • The temperature graph is not well supported by other ice cores but is well supported by the written historical record e.g. farming in Iceland and Greenland, vineyards in the United Kingdom. These warm periods were periods of prosperity and plenty - not dangerous climate crises.
      • The CO2 graph is supported by those from Taylor Dome and Siple Dome. CO2 levels slowly rose from 260 to 280 ppm while temperatures were generally falling.
      • The Sample Intervals in the CO2 graph average about 100 years but occasionally reach 150 years, so the recent rise in CO2 for 60 years might not be "unprecedented".
  • What is good empirical data?
    • A good dataset should be measured by the same instrument throughout. If the instrument is going to be replaced then the old and new should be run in parallel for some time to ensure calibration. If a new instrument must replace a non functioning instrument then an adjustment must be determined and declared.
    • Instruments used in long running datasets must be checked against a higher order calibration instrument to correct for any drift.
    • Conditions surrounding the measurements must remain constant or adjustments determined if conditions are changed.
    • The instrument must be of adequate precision and accuracy to support its use in decision making.
    • Credibility is greatly reduced by splicing data from dissimilar instruments or technologies.
    • If the raw data needs complex processing before giving results in the stated indicator, then the processing should be transparent. Credibility is lost if the processing is changed, particularly if published data is later "refined" to give new conclusions. Alternative groups should be able to replicate the results
    • Empirical data is only relevant to the conditions or location under which it was collected.
  • Atmospheric CO2 concentrations - Apparently driving rising temperatures, so fossil fuel must be replaced by renewables.
    • People don't experience CO2 concentrations directly, so showing monthly mean values is not concealing any life experience.
    • CO2 is assumed to be well mixed around the planet, but Global graphs of CO2 concentration are not seen. Mauna Loa is often shown but Alert Point in the Arctic is where the action is.
    • Map of stations
    • All stations
    • CSIRO's station at Cape Grim, Tasmania
      • Cape Grim vs Baring Head -- similar latitudes
        • Only a slight variation in the results
      • CSIRO's commentary on the Cape Grim CO2 data

        • Carbon dioxide concentrations in the air were reasonably stable (typically quoted as 278 ppm) before industrialisation (in the timeframe of human existence).

          Since industrialisation (typically measured from the mid-18th century), carbon dioxide concentrations have increased by about 40 per cent, based on measurements from Cape Grim and on air samples collected from Antarctic ice at Law Dome.

        • CSIRO have spliced flask/in situ data on to ice core data for CO2. Interestingly, there is temperature data from the Law Dome ice core which shows no correlation between CO2 and temperature.
    • Large graphs
      • Alert Point
      • American Samoa
      • Baring Head
      • Cape Grim
      • Christmas Island
      • Kermadec Island
      • La Jolla
      • Point Barrow
      • Mauna Loa
      • South Pole
    • The CO2 action is in the Arctic
      • PJB: Automatically determine the lag between these pairs.
      • CO2 vs Temperature
      • CO2 vs Eurasian Snow Extent
      • Temperature vs Snow Extent
  • The sample interval of temperature series - Twice Daily records what people actually experienced.
    • Twice daily - Tmin and Tmax This range of temperature was experienced by people near the station on the day in question. Any long term trends or cycles are lost in the huge daily variations.
      • Walgett has the largest range of temperatures in the Australian ACORN SAT V3 network. The inhabitants have been used to 45°C and -5°C since 1910. The smoothed Climate signal is inconsequential.
    • Daily average - An interesting statistic but only representative of a few hours of any day. Any long term trends or cycles are lost in the huge daily variations.

      • Walgett again but now showing daily mean temperatures occasionally hitting 35°C and -5°C.

    • Monthly average - Little relation to what was actually experienced during any day but the seasonal variation swamps any long term signal. The daily and seasonal variation has to be removed before any longer term forecasting is attempted.

      • Walgett again but now showing monthly mean from GHCN V4 Unadjusted. Series now extends back another 30 years to 1880. The minimums now only occasionally reach +5°C rather than the dailys which reached -5°C.

    • Yearly average - No relation to what was actually experienced during any day but with sufficient year to year variation to obscure any long term signal. Probably the best sample interval from which to attempt longer term forecasting.

      • Walgett again but now showing yearly data derived from ACORN SAT V3. The range of temperature show is now only 17°C to 21°C with the smoothed Climate Signal looking much more dramatic.

    • Smoothed yearly average - The Climate Signal looks more dramatic when stripped of any context.

      • Walgett again but now only showing the smoothed averages with no yearly data. First with the same Gaussian smoothing Sigma 2 years as used in the previous graphs.

      • Now with Gaussian smoothing Sigma 30 years for maximum effect, but notice that the range of temperature is only 18.3°C to 18.95°C
  • Geographic averaging of temperature series - Has decreasing relevance to people in specific locations.
    • Individual weather station series - Show actual thermometer values (possibly adjusted)
    • National or regional series - The bigger the nation or region the less representative the average.
    • Hemisphere series - Averages the Tropics with 2 maximums per year through to the Arctic/Antarctic with extremes of day and night. What does this represent?
    • Global series - Averages the Northern Summer with the Southern Winter. What does this represent?
  • Networks of temperature data - Various Organisations provide extensive datasets with the same geographical extents and Sample Intervals.
    • GHCN - Global Historical Climate Network - 27,000+ stations, monthly means, 28% of all GHCN stations show a long term cooling trend
      • In the GHCN maps below, the station dots are red for an increasing temperature trend else blue for a decreasing trend.
      • All stations of any duration (27833)
        • All stations with an increasing temperature trend (19890)
        • All stations with an decreasing temperature trend (7943)
      • Major duration stations 1850 to 2020 (63) Europe and NE USA mostly.
      • Medium duration stations 1900 to 2020 (826) A few appearing in the Southern Hemisphere
      • Minor duration stations 1950 to 2020 (1541) Still mostly northern latitudes but some in the southern.
      • All stations in V4 (21102)
        • The following graphs show the number of GHCN V4 Unadjusted stations active in any year. Stations are assumed to be active from their first year to their last year. The number of active stations peaked in the 1990s. Stations were most common in North America and in the North Temperate zone.
        • Active stations
        • Active stations by Region
        • Active stations by Latitude Band
        • Active stations in Australia
      • All Australian stations (1310)
        • Australian stations with increasing temperature trends (1180) 82%
        • Australian stations with decreasing temperature trends (242) 18%
      • GHCN V1 and V4, Adjusted and Unadjusted - Australia, UK, US, India, China, Russia, Brazil
    • BOM ACORN SAT - Australian surface temperatures from - 112 stations, daily maxs and mins, four versions of adjustments - No stations show a long term cooling trend.
      • The Bureau of Meteorology provides separate files of daily max and min temperatures with some going back to 1910. 112 locations are covered.
      • The max and min files have been combined into a Twice Daily file with the Min at 6am and Max at 6pm. Graphs of this combined file show the actual temperatures experienced at the location and at any time.
      • Any statistical process that averages or smooths this data can then be compared with the original real life experience. Here a linear trend line is shown over the 40+ years of the satellite era. Some examples of the statistical processes might be: anomaly compared to a 30 year baseline, smoothed annual mean. The Climate Crisis signal can be related to real life experience.
      • The thermometers used and the processing applied are probably adequate to meet the needs of the local communities where they were located. The thermometers used in 1910 were never intended to support conclusions based upon the detection of trends of 0.1 degree per century.
      • There are no ACORN SAT stations showing a decreasing temperature trend in spite of the 18% of the Australian GHCN stations showing one.
      • PJB: Show a table of mean, range and trend for each station to show the loss of context in going to an Australian average.
      • PJB: Compare with the 2017 version of ACORN SAT. Is the past most vulnerable to climate change?
      • Map of stations
      • Sydney, Melbourne, Brisbane from 1980
        • Compare the range of temperatures experienced with the smoothed climate line (Gaussian smoothing.)
      • Gabo Island shows the least climate danger from 1910
      • Charleville shows the most climate danger from 1910
      • Horn Island - the most northerly site
        • Surprisingly little daily range until 1995 when something happened to the station.
      • Cape Bruny - the most southerly site
      • Why an Australian average would be worse that using local data
        • Shows the Australian stations sorted by "mean" of the individual monthly mean temperatures. Colours show decile from lowest green to highest red. All but the "min" show little correlation with the "mean".
        • If a decision has to be taken based on the temperature of the climate it is always better to take a local value rather than an Australian average. To use the global average would be ridiculous.
        • |100%
    • HadCRUT - Global and hemispheric monthly means with V2-V5 versions of adjustments
      • HadCRUT5 takes its land component from CRUTEM5 which covers the time period from 1850, with a spatial resolution of 5° latitude by 5° longitude and a monthly-mean time resolution. CRUTEM5 has new quality control procedures to screen for outliers in station data (data showing cooling?). It is assumed that the stations selected for HadCRUT5 come from the 27,000+ GHCN V4 stations. The 5° latitude by 5° longitude gridding process allows the 28% of GHCN stations showing cooling to be lost in the gridding process.
      • No useful information is provided for any local communities. Only global and hemisphere averaged data is available. No gridded time series could be found.
      • The non-infilled version of HadCRUT5 has been selected because each 5x5deg cell must then have at least 1 weather station in it.
      • The thermometers used in the 1850+ time scale were never intended to support the detection of trends per century.
      • HadCRUT5 Global Non-infilled monthly means
        • This data is well modelled by 5 linear trends. The Residuals are quite Random and the last trend could be expected to continue for some time, but the fact that the previous 4 tends all reversed for no obvious reason suggests that 25 year forecasts are impossible on this data.
      • HadCRUT5 Northern and Southern Hemispheres with error bounds
      • HadCRUT5 Northern and Southern Hemispheres compared
      • HadCRUT5 vs CO2
      • HadCRUT ARIMA Forecasts
        • The trend of the forecasts does increase from HadCRUT3 to HadCRUT5 but the confidence limits of the forecasts are so wide that no definitive statement can be made.
        • The statistical character of the three datasets are all similar.
    • UAH Uni of Alabama at Huntsville, Satellite Lower Troposphere monthly means, global, hemispheric, USA, Aust, latitude bands, no adjustments
      • UAH does not change the past data (excellent), but does not provide gridded data so the data is of no use to any decision maker.
      • How do Lower Troposphere temperatures compare to surface temperatures?
        • Satellites don't measure surface temperatures but balloon radiosondes do measure temperatures from the surface and up into the Atmospheric layers.
        • In general Lower Troposphere temperatures match reasonably well with surface ones. At least when averaged over 40deg latitude bands.
        • Delving deeper into the difference between Balloons at the Surface and at the Lower Troposphere
          • For perfect correlation the scatter plots should be on a 45deg line. This can be achieved with enough Gaussian smoothing but relaxing the smoothing to sigma 4years shows some divergence.
      • UAH Stages of Adjustment
        • Lower Troposphere was chosen as the closest to the populated surface.
        • Australia was chosen as one of the few UAH datasets limited by longitude as well as latitude.
        • The Unadjusted dataset comes from the microwave returns from the satellite after much complicated processing which differs between the UAH and RSS organisations.
        • The Unadjusted dataset has a range of -5deg to +5deg ie 10deg.
        • UAH have determined an average adjustment for each month of the year based on a run of 30 years. The same 12 monthly adjustments are used for every year. These adjustments are shown in the Adjustment graph below. They repeat identically every year through a range of about 8deg -4 to +4
        • The final Adjusted dataset is the Unadjusted minus the Adjustment and varies by about 2.5deg being -1 to +1.5deg. Notice the quite large error bounds in gray.
      • UAH LT Australia vs CO2
      • UAH LT Global Land vs Ocean
      • UAH LT by Latitude Band
        • Shows that in polar regions the average Lower Troposphere monthly temperature varies from -8deg to +11deg, a range of 19deg.
        • Shows that in temperate regions the average Lower Troposphere monthly temperature varies from -7deg to +7deg, a range of 14 deg.
        • Shows that in tropical regions the average Lower Troposphere monthly temperature varies from -1deg to +0.5deg, a range of 1.5 deg, with only an occasional excursion.
      • Comparison of UAH Lower Troposphere with HadCRUT5 - The correspondence is good.
  • Comparisons of different interpretations of the same thermometer data - Which version (if any) should you believe?
    • This site's use of the term: weather station
      • There is no unique identifying number for weather stations covered by GHCN V1, GHCN V4, BOM HQ and ACORN SAT. All these temperature time series have latitude and longitude. Therefore latitude and longitude have been used to collect all time series measured at the same geographical point. Some margin is needed to allow for minor variation. Margins of 0.05º, 0.1º and 0.5º have been explored. The bigger the margin the more time series collected within each weather station but the number of weather stations is reduced.
    • Comparing Australian Weather Stations GHCN V1, GHCN V4, BOM HQ and ACORN SAT
      • Series assigned to Weather Stations using 0.05° Lat/Long tolerance.
      • Examining the Maximum Degrees of Adjustment - Average is 0.6°C with some above 2°C
        • In the following we see that only a few series have not had some adjustment.
        • Examining the 16 weather stations with the greatest max difference adjustment.
          • Site data for the Weather Stations in the plots to follow.
      • Examining the Maximum Trend Adjustment - Average is 0.88°C per century but some above 3.5°C
        • Examining the 16 weather stations with the greatest trend adjustment.
          • Site data for the Weather Stations in the plots to follow.
    • Comparing Global Weather Stations - GHCN V1, GHCN V4 Unadjusted, GHCH V4 Adjusted
      • Examining the Maximum Degrees of Adjustment
      • Examining the Maximum Trend Adjustment
  • Comparisons of Pairs of Networks on Australia - Which Network (if any) is most credible?
    • The ARIMA Model: analyses the entire dataset to determine a set of Coefficients that best predict the next value, based on a few (1-3) of the preceding values. The Coefficients capture the "essence" of entire dataset and allow the step by step Prediction of a new noiseless dataset with estimates of accuracy. The "essence" can be seen in the Impulse Response of the ARIMA Model. The Model also provides an estimate of the noise that has been removed and also guarantees the Residual errors between Predictions and Observations are minimum and random unlike smoothing and filtering or simple trend fitting.
    • Forecast Trend Change: The ARIMA Model Coefficients are a basis for Forecasting beyond the end of the observed dataset. The forecasts will be influenced by the last few values in the dataset but also by the "essence" of all values. The Forecast Confidence Limits are based on 5% limits. The difference in the 30 year Forecast values for both Network Pairs is converted into a Trend Change.
    • Max Difference: The step by step Predictions of both Networks of the Pair are compared at every value, with the Maximum being returned.
    • SD_Residuals: The ARIMA Model minimises the Residual errors between Predictions and Observations and accumulated then into a Variance. The square root of this is the Standard Deviation of the Residuals. It is a measure of the noise underlying the observations.
    • Shock Change: Part of the "essence" captured by the Coefficients can be seen in the Impulse Response. When the unit Impulse does not return to the original value, here it is call the "Shock". The Shock Change is the difference between the two Networks of the Pair.
    • Start Pruned: The average difference beween the Start Years of Datasets of the two Networks in the Pair. A large difference in Start Years will affect the "essence" of the Coefficients.
    • End Extended: The average difference beween the End Years of Datasets of the two Networks in the Pair. Difference in End Years can have a great effect on Forecasts.
    • GHCN_V1 to GHCN V4 Unadjusted
      • |75%
    • GHCNV1 to BOMHQ
      • |75%
    • GHCNV1 Unadjusted to ACORN2023
      • |75%
      • |75%
    • BOMHQ to ACORN2017
      • |75%
      • |75%
    • BOMHQ to ACORN2023
      • |75%
    • ACORN_2017 to ACORN 2019
      • |75%
      • |75%
    • ACORN_2019 to ACORN 2023
      • |75%
    • GHCN_V4 Unadjusted to Adjusted
      • |75%
  • Comparisons of Pairs of Global Networks -
    • |75%
    • |100%
    • GHCN_V1 to GHCN V4 Unadjusted
      • |75%
      • |75%
    • GHCN_V4 Unadjusted to Adjusted
      • |75%
    • Comparisons by Country
      • GHCN_V1 to GHCN V4 Unadjusted
      • GHCN_V4 Unadjusted to Adjusted
  • Exploring a Statistical Model -
    • Statistical Model characteristics - How far can a time series forecast the future?
      • Validity of a Statistical Model - Valid only when the Residuals between the incremental predictions and the observed data are entirely random. This is almost never the case with temperature time series.
      • A Random Walk with no long term mean is implied by the presence of a Unit Root from the Augmented Dickey Fuller statistic. The Hurst Persistence statistic may also show a Random Walk. Apparent trends may occur during a Random Walk due to chance.
      • Serial Correlation - If present it invalidates the use of simple linear trend models. It is detected by the Durban Watkins statistic.
    • Summarised Characteristics
      • Long term mean
      • No long term mean
    • Statistical Model Characteristics of Networks
    • Exploring the ARIMA Model
  • Some Observations
    • ARIMA SD_Residuals are related to Latitude.
      • |75%
      • |75%
  • Ocean Temperatures
    • Network GTMBA
    • Nino3-4 region of the equatorial Pacific
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