The retrodiction and prediction of solar activity are two closely-related problems in dynamo theory. We applied Machine Learning (ML) algorithms and analyses to the World Data Center’s newly ...constructed annual sunspot time series (1700–2019; Version 2.0). This provides a unique model that gives insights into the various patterns of the Sun’s magnetic dynamo that drives solar activity maxima and minima. We found that the variability in the ~11-year Sunspot Cycle is closely connected with 120-year oscillatory magnetic activity variations. We also identified a previously under-reported 5.5 year periodicity in the sunspot record. This 5.5-year pattern is co-modulated by the 120-year oscillation and appears to influence the shape and energy/power content of individual 11-year cycles. Our ML algorithm was trained to recognize such underlying patterns and provides a convincing hindcast of the full sunspot record from 1700 to 2019. It also suggests the possibility of missing sunspots during Sunspot Cycles −1, 0, and 1 (ca. 1730s-1760s). In addition, our ML model forecasts a new phase of extended solar minima that began prior to Sunspot Cycle 24 (ca. 2008–2019) and will persist until Sunspot Cycle 27 (ca. 2050 or so). Our ML Bayesian model forecasts a peak annual sunspot number (SSN) of 95 with a probable range of 80–115 for Cycle 25 between 2023 and 2025.
Correlation and correlation‐based measures (e.g., the coefficient of determination) have been widely used to evaluate the “goodness‐of‐fit” of hydrologic and hydroclimatic models. These measures are ...oversensitive to extreme values (outliers) and are insensitive to additive and proportional differences between model predictions and observations. Because of these limitations, correlation‐based measures can indicate that a model is a good predictor, even when it is not. In this paper, useful alternative goodness‐of‐fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation‐based measures are discussed. Modifications to these statistics to aid in interpretation are presented. It is concluded that correlation and correlation‐based measures should not be used to assess the goodness‐of‐fit of a hydrologic or hydroclimatic model and that additional evaluation measures (such as summary statistics and absolute error measures) should supplement model evaluation tools.
Harmonic analysis of a time series of National Oceanic and Atmospheric Administration (NOAA) advanced very high resolution radiometer normalized difference vegetation index (NDVI) data was used to ...develop an innovative technique for crop type identification based on temporal changes in NDVI values. Different crops (corn, soybeans, alfalfa) exhibit distinctive seasonal patterns of NDVI variation that have strong periodic characteristics. Harmonic analysis, or Fourier analysis, decomposes a time-dependent periodic phenomenon into a series of constituent sinusoidal functions, or terms, each defined by a unique amplitude and phase value. Amplitude and phase angle images were produced by analysis of the time-series NDVI data and used within a discriminant analysis to develop a methodology for crop type identification. For crops that have a single distinct growing season and period of peak greenness, such as corn, the majority of the variance was captured by the first and additive terms, while winter wheat exhibited a bimodal NDVI periodicity with the majority of the variance accounted for by the second harmonic term.
Precipitation measurements in the United States (as well as all other countries) are adversely affected by the gauge undercatch bias of point precipitation measurements. When these measurements are ...used to obtain areal averages, particularly in mountainous terrain, additional biases may be introduced because most stations are at lower elevations in exposed sites. Gauge measurements tend to be underestimates of the true precipitation, largely because of wind-induced turbulence at the gauge orifice and wetting losses on the internal walls of the gauge. These are not trivial as monthly estimates of this bias often vary from 5% to 40%. Biases are larger in winter than in summer and increase to the north in the United States due largely to the deleterious effect of the wind on snowfall. Simple spatial averaging of data from existing networks does not provide an accurate evaluation of the area-mean precipitation over mountainous terrain (e.g., over much of the western United States) since most stations are located at low elevations. This tends to underestimate area averages since, in mountainous terrain, precipitation generally increases with elevation. Temporal precipitation trends for the United States, as well as seasonal and annual averages, are presented. Estimates of unbiased (or less biased) precipitation over the northern Great Plains provide a regional analysis.
The objective of this study was to determine whether the CERES-Maize crop simulation model can be used to predict rainfed corn yield up to 4 months prior to harvest. Required weather data were ...provided by combining the observed weather conditions up until the time of the forecast with predictions of future weather conditions. Yield forecasts were issued four times (June 1, July 1, August 1, and September 1) during each growing season (2001, 2002, and 2003). Thirty future weather scenarios were generated for each forecast date based on historical weather conditions and these forecasts were validated using the full season simulations. Not surprisingly, forecasts issued later in the growing season were more accurate than forecasts issued earlier because they incorporate more of the actual weather conditions. It appears that both the June 1 and July 1 forecasts would be of little value to decision makers because they contain too much uncertainty. However, the August 1 yield forecasts were more promising, the mean absolute error (MAE) ranged from 471 to 2407
kg
ha
−1 and the mean percent error (MPE) ranged from 4.8 to 46.6% and all of the September yield forecasts were extremely accurate. The September forecasts were very accurate (MAE ranged from 29 to 135
kg
ha
−1 and the MPE ranged from 0.5 to 1.3%) because the crop has often reached maturity by September 1. CERES-Maize would be useful to agencies that require accurate yield estimates prior to harvest. If reliable predictions of future weather conditions are available, CERES-Maize can be employed to accurately forecast yield months before harvest.
We have examined the evidence presented by Labat et al. and found that (1) their claims for a 4% increase in global runoff arising from a 1
°C increase in air temperature and (2) that their article ...provides the “first experimental data-based evidence demonstrating the link between the global warming and the intensification of the global hydrological cycle” are not supported by the data presented. Our conclusions are based on the facts that (1) their discharge records exhibit non-climatic influences and trends, (2) their work cannot refute previous studies finding no relation between air temperature and runoff, (3) their conclusions cannot explain relations before 1925, and (4) the statistical significance of their results hinges on a single data point that exerts undue influence on the slope of the regression line. We argue that Labat et al. have not provided sufficient evidence to support their claim for having detected increases in global runoff resulting from climate warming.
Several quantitative estimates of surface instrumental temperature trends in the late 20th century are compared by using published results and our independent analyses. These estimates highlight a ...significant sensitivity to the method of analysis, the treatment of data, and the choice of data presentation (i.e., size of the smoothing filter window). Providing an accurate description of both quantitative uncertainties and sensitivity to the treatment of data is recommended as well as avoiding subjective data‐padding procedures.
Several recent studies claim to have found evidence of large‐scale climate changes that were attributed to human influences. These assertions are based on increases in correlation over time between ...general circulation model prognostications and observations as derived from a centred pattern correlation statistic. We argue that the results of such studies are inappropriate because of limitations and biases in these statistics which leads us to conclude that the results of many studies employing these statistics may be erroneous and, in fact, show little evidence of a human fingerprint in the observed records.
Long-term warming of late spring (April–June) air temperatures has been proposed by Stirling et al. Stirling, I., Lunn, N.J., Iacozza, J., 1999. Long-term trends in the population ecology of polar ...bears in western Hudson Bay in relation to climatic change. Arctic 52, 294–306 as the “ultimate” factor causing earlier sea-ice break-up around western Hudson Bay (WH) that has, in turn, led to the poorer physical and reproductive characteristics of polar bears occupying this region. Derocher et al. Derocher, A.E., Lunn, N.J., Stirling, I., 2004. Polar bears in a warming climate. Integr. Comp. Biol. 44, 163–176 expanded the discussion to the whole circumpolar Arctic and concluded that polar bears will unlikely survive as a species should the computer-predicted scenarios for total disappearance of sea-ice in the Arctic come true. We found that spring air temperatures around the Hudson Bay basin for the past 70 years (1932–2002) show no significant warming trend and are more likely identified with the large-amplitude, natural climatic variability that is characteristic of the Arctic. Any role of external forcing by anthropogenic greenhouse gases remains difficult to identify. We argue, therefore, that the extrapolation of polar bear disappearance is highly premature. Climate models are simply not skilful for the projection of regional sea-ice changes in Hudson Bay or the whole Arctic. Alternative factors, such as increased human–bear interaction, must be taken into account in a more realistic study and explanation of the population ecology of WH polar bears. Both scientific papers and public discussion that continue to fail to recognize the inherent complexity in the adaptive interaction of polar bears with both human and nature will not likely offer any useful, science-based, preservation and management strategies for the species.
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