Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901–2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011–2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data.
Data Availability: The data are delivered through the following three options: 1) a software package, ClimateNA (http://cfcg.forestry.ubc.ca/projects/climate-data/); 2) a Google Map based interface (http://climatewna.com/climatena_map/); 3) raster surface files (http://adaptwest.databasin.org/pages/adaptwest-climatena).
Funding: Funding was received from the Forest Genetic Council of British Columbia, Canada, and from the Competitiveness and Innovation Branch of the Ministry of Forests, Lands and Natural Resources Operations of British Columbia, Canada. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
With increasing importance of climate change related research and applications in mitigation and adaptation, the demand for accurate and accessible climate databases is high. Historical climate data are necessary for understanding the relationships between climate and biological response of organisms, or general patterns of ecological adaptations to local climate environments. Such insight can be used to build mechanistic or statistical models in ecology and other fields of study [1, 2]. Future climate projections from general circulation models can then be used to predict the potential impact of climate change and provide information for developing adaptive strategies to address a changing climate.
For ecological modeling, climate data are required to represent the climate conditions as close as possible to the locations where organisms reside [3, 4]. However, climate data generated by general circulation models (GCMs)  are at a coarse spatial resolution (100 ~ 300 km) and do not meet such requirements. Climate data from regional climate models (RCMs) or Global Time Series at Climate Research Unit (CRU)  have higher spatial resolutions (10 ~ 50 km), but they are still far too coarse to characterize climate habitat for organisms in complex landscape, such as mountainous areas. For these types of applications, climate data at moderate spatial resolutions (800 ~ 4000 m) are generated by interpolating observations from weather stations and overlaying projections from general circulation models with the delta method. WorldClim , for example, provides a large number of climate variables for the reference period 1950–2000 at various spatial resolutions generated using the ANUSPLIN interpolation method . Another widely used database is based on the PRISM interpolation method , which produces climate data for the United States and some other regions using a combination of a statistical approach and an expert knowledge-based adjustment to consider rain shadows, coastal effects, and winter temperature inversions in mountainous regions. Mosier et al.  have recently generated global time series at 800 m for monthly total precipitation and mean temperature through downscaling monthly time series data from CRU, M&W and Global Precipitation Climatology Centre .
A general disadvantage of gridded climate databases such as WorldClim or PRISM is that they are quite large, so that extracting relevant information for a set of sample points or a local area of interest can be tedious and requires advanced skills in working with geographic information systems (GIS). Secondly, they are fundamentally limited in characterizing sample points that have inaccurate spatial information (for example, reported to the nearest minute). Such errors in spatial accuracy are particularly problematic in steep mountainous terrain, where a medium-resolution grid cell would still span climate environments with several hundred meters difference in elevation. We have addressed this problem previously with a software solution ClimateWNA , which builds on PRISM and ANUSPLIN data to generate scale-free climate data through a combination of bilinear interpolation and elevation adjustments. Bilinear interpolation interpolates the gridded baseline data into a seamless surface, while the elevation adjustment improves the prediction accuracy at specific locations using a digital elevation model or recorded sample elevation. Elevation adjustments are implemented by empirical lapse rates that vary among variables, location, and elevation. They are calculated as partial derivative functions with respect to elevation from polynomial functions that reflect the relationship between a monthly climate variable and latitude, longitude, elevation and their transformations and interactions [13, 14].
Our lapse rate adjustment approach is applied to 36 basic climate variables that are widely available and measured according to standardized methods at weather stations worldwide. These variables include average monthly minimum and maximum temperature and monthly precipitation. Most biological or ecological modeling applications do not rely on those primary monthly climate variables directly [15–17]. We therefore also provide 24 climatic variables that are relevant in ecology (bioclimatic variables) or that are relevant for other applications, such as infrastructure planning. Some important derived climate variables, such as growing season precipitation, can easily be calculated from monthly data, but others such as growing degree-days and frost-free period require daily climate data to calculate. Rather than including daily data in the software package, which would increase its size by two orders of magnitude, we found that it is possible to estimate variables that summarize daily data (such as growing degree days over the course of a year) from monthly data with high accuracy . These algorithms are also included in the ClimateWNA software package.
Like other datasets, we make use of the delta method to overlay a medium resolution (4km) baseline dataset with historical anomaly data that are provided at about 50km resolution, and projections from GCMs that range from 100 to 300km resolution. The advantage of our approach is that the software executes these overlays on demand for sample points or local areas. Consequently, the comprehensive climate database of more than 20,000 spatial layers of historical and future data that is included with the software package remains small and can be queried instantly (or within seconds or minutes for complex queries) on a regular personal computer. Our implementation of the delta method is slightly different from others in that we replace the medium resolution baseline with scale-free data generated by ClimateWNA while the anomaly is bilinearly interpolated to match the scale-free baseline data, so that the output of ClimateWNA for historical or future period is also scale-free (i.e. not gridded but directly estimable for any location). The approach has been shown to considerably improve the statistical accuracy of climate estimates when compared to regular climate grids, when tested against original weather station data .
Our previous software packages have been extensively used in ecology, hydrology, forestry, agriculture, and urban planning in western North America. Notably, users include university researchers, consultants, government planners and industry managers with sizable portion of citations for data use in the “gray literature” such as government publications, extension publications, internal and consultant reports. Here, we contribute an extension of our data coverage to the entire North American continent. We also developed algorithms to improve accuracy and to extend the functionality of the new ClimateNA software package. Specifically, this release includes the following new developments: 1) a new 4km resolution baseline climate layer for 36 basic variables for the entire contiguous North American continent; 2) new dynamic local downscaling algorithms, which are broadly applicable and sensitive to local-scale variations in lapse rates; 3) additional biological relevant climate variables at monthly and seasonal scales; and 4) an integration of downscale gridded climate data for historical, paleo and future years and periods. In this paper, we also validate the effectiveness of the improved downscaling approaches and the accuracy of new climate variables against observations from weather stations.
We used the monthly temperature and precipitation data for the normal period of 1961–1990 as a baseline data for the development of ClimateNA. The baseline dataset was compiled from several data sources listed in Table 1, which were resampled to a uniform 2.5 arcmin (approximately 4 km) resolution grid, and adjusted to 1961–1990 normal period as explained below. The original datasets were at various spatial resolutions and for two different normal periods. Unifying the data to a lower resolution (4 x 4 km) was necessary due to the size of the dataset for such a large region and has ultimately little effect on the accuracy of climate estimates because our lapse-rate based downscaling methodology is designed to recover elevation-driven spatial variation from lower resolution grids. We use the standard normal period from 1961–1990 defined by the World Meteorological Organisation (WMO) as baseline data for three reasons. First, spatial weather station coverage is the best for this period to allow for the development of a reliable interpolated baseline dataset. Second, to keep the reference period unchanged from our previous packages and to serve as the anchor for the observation of the long-time climate development. Third, the period is a useful reference in ecological research because it precedes a significant anthropogenic warming signal . However, our software packages allow users to generate climate data for other normal periods including 1981–2010.
Table 1. Sources of climate data used to generate the baseline climate normal (1961–1990) grids for the ClimateNA software package.
For adjusting the period from 1971–2000 to 1961–1990, we first developed anomalies for 1971–2000 relative to 1961–1990 using CRU data (described below) and bilinearly interpolated to the same spatial resolution as the target baseline dataset. The adjustment was achieved by subtracting the anomalies from the 1971–2000 normal data. The 1961–1990 dataset is a mosaic of 5 datasets covering the lower 49 states (PRISM), British Columbia and the Yukon Territories (PRISM), Alaska (PRISM), Alberta, Saskatchewan, and Manitoba below 55° latitude (PRISM), Northern and eastern Canada (WorldClim) and Mexico (WorldClim). The coverages were trimmed to an overlap of 50km, and were then merged with a gradient tool. An equal weight (50%) was allocated to each of the two overlapping datasets at the center of the overlap, and the weight was gradually reduced to 0 at the boundary of each dataset. The complete baseline dataset comprised average monthly maximum and minimum temperatures and monthly total precipitation for a total of 36 primary monthly climate variables, plus the mean elevation of each grid cell (see Table 1).
Monthly temperature and precipitation data for recent years were obtained from historical time series data generated at the Climate Research Unit (CRU) at the University of East Anglia . The version incorporated in the current ClimateNA package is CRU Ts3.23 . The spatial resolution of the data is 0.5 x 0.5° and covers the period of 1901–2014 (to be updated when available). The original data were developed based on anomalies relative to the reference period 1961–1990, but absolute values were subsequently derived for each individual year . We recover the original anomalies by subtracting the 1961–1990 CRU average from the annual climate layers.
The monthly temperature and precipitation data for future periods were from General Circulation Models (GCMs) of the Climate Model Intercomparison Project 5 (CMIP5) corresponding to the Fifth Assessment Report of the Intergovernmental Panel for Climate Change . Fifteen GCMs were selected that represent all major clusters of similar AOGCMs . We included two greenhouse gas concentration trajectories or Representative Concentration Pathways (RCP): RCP 4.5 and RCP 8.5 . In RCP 4.5, emissions peak in the 2040s and then decline. RCP 8.5 assumes that emissions continue to increase throughout the century. We omitted an optimistic and intermediate scenario with a peak in emissions around 2020 and 2080. When multiple runs were available for each GCM, an ensemble average was taken over the available runs (but limited to a maximum of five). We summarized annual GCM projections into 30-year time periods that we hereafter referred to as 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100). Time-series of annual projections were also included for the years between 2011–2100 for six of the selected 15 GCMs and the two RCPs. For the time-series, we only used the first run for each model and RCP to keep its original variability.
General circulation models are routinely tested for their realism through their hindcasting abilities. For paleoecological research, such hindcasts provide valuable climate information that we have also included. For paleoclimate, monthly data were obtained for the Last Glacial Maximum (LGM, 21,000 years ago), Mid-Holocene (6,000 years ago) and Last millennium (1,000 years ago) from four GCMs of the CMIP5. We took the averages of monthly climate projections over the first 50 years of each paleoclimatic period (essentially a point sample in a paleoclimatic context), which was the minimum period available among the four GCMs.
As GCM data are available at various spatial resolutions, ranging from 0.75 x 0.75° through 2.85 x 2.85°, we interpolated the GCM data to the resolution of 1 x 1° using bilinear interpolation for simple integration into ClimateNA. To implement the delta method just as described for overlaying the monthly historical anomalies, we also converted GCM projections to anomalies by subtracting the average of GCM projections for the 1961–1990 reference period.
Instead of using the midpoint values of each grid cell to represent its entire area, we used a combination of bilinear interpolation and local elevation adjustment approaches to downscale the baseline monthly grid data (4 × 4 km) to scale-free point data. The program first uses bilinear interpolation to estimate values between midpoints of the four neighbor grids to generate a seamless surface for each monthly climate variables, avoiding step-artifacts at grid boundaries. The algorithms then retrieve monthly climate data and elevation values for a location from the corresponding grid cell plus eight surrounding cells. The climate and elevation values of the nine cells are used to calculate differences in a climate variable and in elevation between all 36 possible pairs. A simple linear regression of the differences in the climate variable on the difference in elevation is then established, and the slope of the regression is used as the empirical lapse rate for each climate variable at each specific location. As the local regressions are dynamically developed along with locations of inquiry, we call this downscaling method a “dynamic local downscaling” approach. To avoid over-adjustments due to a weak linear relationship, each lapse rate was weighted by the R-square value of the local linear regression.
Historical, paleo and future anomaly data are also downscaled prior to applying the delta method, using bilinear interpolation to create seamless surfaces. The interpolated anomalies were then added onto the downscaled baseline monthly climate normal data (scale-free) to arrive at the final climate surface at a specified resolution or for point data. With this approach, the original baseline portion (i.e., absolute values for the 1961–1990 normal period) of the historical and future climate data were replaced by the scale-free climate data generated by ClimateNA. Because elevation-adjusted baseline data generated by ClimateNA typically has much higher spatial resolution than the historical and future climate data, this process preserves the accuracies for both historical and future climate data with respect to accurately representing climate gradients that are driven by local topography (assuming that they will have similar effects in the past and future). However, the approach does not change or improve GCM anomaly projections or the historical anomalies data per se.
The baseline dataset contains 36 primary monthly climate variables. For applications in ecology, we provide many additional biologically relevant climate variables. Many of these additional variables need to be calculated using daily climate data, which are not available in ClimateNA. We estimated these variables based on empirical or mechanistic relationships between these variables calculated using daily observations and monthly climate variables from weather stations across the entire North America. We called these variables “derived climate variables”. Some of them have been developed in previous studies for smaller regions at the annual scale [12, 13]. In this study, we developed the derived climate variables at monthly scale, then summed up to seasonal and annual scales. The steps included: 1) calculating derived climate variables for each month (e.g., degree days) from daily weather station data; 2) building relationships (or functions) between the derived climate variables and observed (or calculated) monthly climate variables; 3) applying the functions in ClimateNA to estimate derived climate variables using monthly climate variables generated by ClimateNA.
Observed daily climate data were obtained from 4,891 weather stations in North America from the Daily Global Historical Climatology Network (http://www.ncdc.noaa.gov). The distribution of the weather stations is shown in Fig 1. Due to the wide range of variation in climate in North America, no single linear, polynomial or nonlinear function was found to adequately reflect the relationships between degree-days and monthly climate variables. We therefore applied piecewise functions, which combine a linear function and a nonlinear function, to model these relationships between various forms of monthly degree-day variables and monthly temperatures. The degree-day variables include degree-days below 0°C (DD < 0), degree-days above 5°C (DD>5), degree-days below 18°C (DD<18) and degree-days above 18°C (DD>18). The general form of the piecewise functions of all degree-days (DDm) is: (1) where, Tm is the monthly mean temperature for the m month; k, a, b, T0, c and β are the six parameters to be optimized.
Fig 1. Distribution of 4891 weather stations and the baseline data sources (PRISM and WorldClim) within the coverage of ClimateNA.
For number of frost-free days (NFFD) and precipitation as snow (PAS), a sigmoid function was used to model the relationship between these monthly variables and monthly temperatures: (2) where, Tm is the monthly minimum temperature for the m month; a, b and T0 are the three parameters to be optimized.
To estimate the length of the frost-free period (FFP), the beginning the frost-free period (bFFP) and the end of the frost-free period (eFFP), we used the same polynomial functions as ClimateWNA  for bFFP and eFFP while the parameters were estimated based on observations from all weather stations in North America.
For extreme minimum temperature (EMT) and extreme maximum temperature (EXT) expected over a 30-year period, polynomial functions were used as follows: (3) (4) where, a, b, c, d, e and f are the parameters to be optimized; Tmin01 and Tmin12 are monthly minimum temperature for January and December; Tmax07 and Tmax08 are monthly maximum temperature for July and August, respectively; and TD is continentality (the difference between the mean temperatures of the warmest and coldest months).
The accuracies of the climate variables generated by ClimateNA were evaluated against observations from 4,891 weather stations shown in Fig 1. Observed monthly normals of the primary climate variables for the reference period (1961–90) were calculated based on the daily climate data from the weather stations. The prediction errors, defined as the mean absolute error (MAE), were used to evaluate the accuracy of the climate variables generated by ClimateNA for the baseline data. We also compared the prediction accuracies between ClimateNA output and the original PRISM (800 x 800 m) for areas with the PRISM data available (US and BC) against 4,257 weather stations within the coverage. It should be noted that these assessments are not truly independent validations of the original data layers, as many of these station data were included in the development of the original PRISM and WorldClim grids. Rather, the accuracy assessments evaluate the relative improvements achieved by our downscaling algorithms.
The regressions of climate variables as a function of elevation, on which dynamic local linear downscaling approach relies, typically explained around 65% of the variance in monthly minimum temperature (Tmin), 75% in monthly maximum temperature (Tmax), and 35% in monthly precipitation (PPT) (Fig 2). The amount of variance explained varied from location to location, being greater in mountain areas, where elevation adjustment is needed, and smaller in flat areas, leading to a wide range of local R2 values as indicated by the box plots (Fig 2). This approach allows for high flexibility to model spatial and temporal changes of lapse rates. As an illustration of how they may vary, Fig 3 shows the changes in the lapse rate (the slope of the regression line) from January through March for a grid cell in a mountain valley, where the positive slope for Tmin01 reflects temperature inversions in mid-winter.
Fig 2. Proportions of variance explained (R square values) by local linear regression in total variation among the nine neighbor pixels for monthly minimum and maximum temperatures, and monthly precipitation across the entire North America.
Fig 3. Relationships between differences in minimum temperatures in January (Tmin01), February (Tmin02) and March (Tmin03) and the change in elevation among the nine neighbor cells at a randomly picked mountain area in British Columbia (latitude = 53.88° and longitude = -125.1°).
The dynamic lapse rate adjustment implemented by ClimateNA considerably reduced the prediction errors for monthly maximum temperatures by 5–18% (Table 2). However, the improvements for monthly minimum temperature (2–5%) and precipitation (0–3%) variables were smaller. The prediction accuracy of ClimateNA was comparable to the original PRISM (800 x 800 m), being almost identical for monthly temperatures, while the former was slightly better than the latter for monthly precipitation variables (data not shown).
Table 2. Comparisons in prediction standard errors between ClimateNA and the baseline climate data for primary monthly climate variables based on evaluations against observations from 4891 weather stations in North America.
The relationships between monthly data and biologically relevant climate variables derived from daily weather station data could generally be well described by the piecewise functions or nonlinear functions (Figs 4 and 5). However, the relationships showed distinct patterns between the west and the east for growing degree days (DD>5°C) and between the southwest and the rest for DD>18°C. Thus, the functions for these two variables were built separately by region to better capture relationships as illustrated in Fig 5.
Illustrations of the model fit (red line) of the piecewise function (linear and nonlinear) for monthly degree-days DD<0 and DD<18 on monthly mean temperature for January for the base line period 1961–1990 for 4891 locations (black points).
Illustrations of the model fit (red line) of the piecewise function (linear and nonlinear) with all (4891) and split samples for monthly degree-days DD>5 and DD>18 on monthly mean temperature for January for the base line period 1961–1990.
The amount of variance explained and the prediction errors for all biologically relevant climate variables derived from observed daily climate data are listed in Table 3. The functions (S1 File) and their parameters as well as the results of evaluations (S1–S6 Tables) are listed in the Supporting Information. In most cases, the predictive accuracy of monthly variables for derived variables is very high with R2 values larger than 0.95 (S1–S5 Tables). We expect that accuracies for derived variables that are derived for individual months is somewhat lower than for annual derived variables, and that is generally confirmed (note that the annual MAEs must be divided by 12 to be directly comparable with the monthly MAEs). Our lowest R2 values are for precipitation as snow (PAS) at a monthly time scale (S6 Table).
Table 3. The amount of variance in observed climate variables explained by ClimateNA derived variables and their prediction standard errors.
Elevation is the dominant factor affecting temperature at a local scale, but the effect of elevation on temperature and precipitation varies with latitude, local terrain and other factors. Temperature inversions (i.e., temperature is cooler at lower elevations) can also occur in some areas, typically in winter months. Therefore, it remains a challenge to develop global functions that accurately reflect lapse rates across a large region . We found that the dynamic local regression approach developed in this study is effective for monthly temperature and precipitation variables in downscaling the baseline climate data at a moderate spatial resolution. In most cases, over 60% of the total variation in temperature among the nine neighbor tiles of the baseline climate can be explained by elevation, and it is more effective in mountain areas (>90%) where elevation adjustment is more critical than in flat areas. This approach is able to capture the elevation effect in case of temperature inversion. The amount of variation explained by elevation in monthly precipitation is smaller (~30% for most cases, >40% in mountain areas), and elevation adjustment is not possible at all using the partial derivative functions used in ClimateWNA (up to version 4.85), so the dynamic downscaling still provides a significant improvement for precipitation over previous versions of the software.
New derived climate variables are an important addition to the ClimateNA software package. Climate-driven ecological and hydrological models require driving variables that represent periods longer than a month, or derived climate variables that represent summaries of daily data over a longer period. These variables are usually referred to as bioclimatic variables and are widely used in ecological modeling. For example, the most important climate variable that separating forest ecosystems in British Columbia is Continentality (TD) . Precipitation as snow (PAS) and summer heat-moisture index (SMH) have been the most effective climate variables to delineate the hybrid index for hybrids between white and Engelmann spruces . Various forms of degree-days, frost-free days and moisture deficit index are also often found to play important biological [24, 25], ecological [1, 26] and hydrological  roles. ClimateNA, with added derived climate variables at monthly and seasonal levels, will provide more options for modellers to test, identify, and make use of climate variables that closely represent drivers of ecological processes.
ClimateNA makes it possible to generate climate data at any desirable spatial resolution. To give an example for the need of downscaling in ecological research, we visualize how climate surfaces match the topography (Fig 6A, 6C and 6E), and how well the predicted forest ecotypes match the originally mapped ecosystem classifications (Fig 6B, 6D and 6F) at different spatial resolutions (800 x 800 m and 80 x 80 m). For predicting forest ecotypes, we used ClimateNA to generate climate variables for a bioclimate envelope model of forest ecosystems in British Columbia . Local climate gradients in topographically complex terrain can be a driver of forest ecotypes at very fine spatial resolutions that the ClimateNA software package can deliver (here, 80 x 80 m). For the predicted ecotypes in particular, the finer spatial resolution is critical at a forest management scale (Fig 6B, 6D and 6F). The coarse resolution is not able to represent the spatial distributions of the original ecotypes at the local scale.
Landscape image (a) and original forest ecotype map (b) at the north end of Vancouver Island, BC, Canada (centered at Lat. = 50.362° and Long. = -126.385°). Spatial distributions of mean annual temperature (MAT) (c and e) and predicted forest ecotypes (d and f) at the spatial resolutions of 800 x 800 m (c and d) and 80 x 80 m (e and f), respectively.
Notwithstanding the need for high-resolution climate grids, we should also point out the general limitations of the data we provide through the software package and its underlying databases. Although climate data can be generated at very fine resolution as shown in the above example, the data are ultimately based on interpolated data from standardized weather stations, and therefore cannot include micro site effects that are driven by vegetation, water bodies, or other micro-scale physiographic features.
While the dynamic lapse-rate algorithms considerably improve on regular gridded climate data, the accuracy of the climate estimates also vary with the time period the variable represents and the variable type. As shown in the results, errors tend to be largest in complex derived variables that are not averaged over longer time periods. For example, measurements of the degree days for an individual month of an individual year come with larger uncertainties than a degree day estimate that is made for the entire year or estimates for a month that are averaged over a 30-year normal period.
Lastly, we expect variable estimates with larger errors in the remote areas where the number of weather stations is limited as shown in Fig 1. Because our validations are not independent (i.e. the weather stations we use for validation here have likely also been used by the PRISM and WorldClim groups for developing the baseline data), the average prediction error was quite small with an average of 0.77°C for monthly temperatures. However, this does not necessarily guarantee that estimates for areas without nearby weather stations have this level of precision.
Three options were developed for accessing the downscaled climate data generated by the algorisms implemented in ClimateNA: 1) ClimateNA desktop package; 2) ClimateNA Map; and 3) a GIS raster data portal.
Finally, we provide gridded raster data layers that were generated for the entire North America at the spatial resolution of 1000 x 1000 meter for the normal periods 1961–1990 and 1981–2010, and three future periods 2020s, 2050s and 2080s for 8 selected CMIP5 models and two greenhouse gas emission scenarios (RCP4.5 and RCP8.5). These datasets can be downloaded at http://tinyurl.com/ClimateNA or http://adaptwest.databasin.org/pages/adaptwest-climatena. The variables can also be displayed graphically, as summarized by watershed across North America, using an interactive online data viewer (http://adaptwest.databasin.org/app/watershed-climate-explorer). Additional variables derived from ClimateNA output, such as climate velocity , can also be downloaded from the portal (http://adaptwest.databasin.org/pages/adaptwest-velocitywna).
In summary, we found that the dynamic local-regression approach implemented in ClimateNA can effectively downscale the gridded baseline data into scale-free point data with improved prediction accuracy for all climate variables. The software package facilitates access to climate data at both large scales (e.g. for continental species ranges and regional ecosystem characterization and modeling), but also at local management unit scales (e.g. to climatically characterize plot and sample data). Additions of derived biologically and ecologically relevant climate variables at monthly and seasonal scales provide more options for modellers to improve their biological, ecological and hydrological models. Integration of historical, future and paleo climate data through the delta downscaling method provides convenience through integrating many climate data sources in one package. Time-series functions, interactive map-based interface and raster data portals may save users the considerable effort of processing large volume of climate data. We hope that ClimateNA will serve as a useful tool in climate related research and applications across North America under changing climate.
S1 File. Equations of derived climate variables.
S1 Table. Parameters and the results of the model fit for monthly Degree-days below 0°C (DD < 0).
S2 Table. Parameters and the results of the model fit for the piecewise function for monthly Degree-days above 5°C (DD > 5).
S3 Table. Parameters and the results of the model fit for the piecewise function for monthly Degree-days below 18°C (DD < 18).
S4 Table. Parameters and the results of the model fit for the piecewise function for monthly Degree-days above 18°C (DD > 18).
S5 Table. Parameters and the results of the model fit for monthly Number of frost-free days (NFFD).
S6 Table. Parameters and the results of the model fit for monthly precipitation as snow (PAS).
This study was funded by Forest Genetic Council of British Columbia, Canada and Competitiveness and Innovation Branch, Ministry of Forests, Lands and Natural Resources Operations of British Columbia, Canada.
Conceived and designed the experiments: TW AH DS. Performed the experiments: TW AH DS CC. Analyzed the data: TW AH DS CC. Contributed reagents/materials/analysis tools: TW AH DS CC. Wrote the paper: TW AH DS CC.
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19. Harris I, Jones PD, Osborn TJ, Lister DH. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int J Climatol. 2014;34(3):623–42. pmid:WOS:000332092900008.
20. Knutti R, Masson D, Gettelman A. Climate model genealogy: Generation CMIP5 and how we got there. Geophys Res Lett. 2013;40:1194–9.
21. EWRI ASCE. The ASCE Standardized Reference Evapotranspiration Equation.: Environmental and Water Resources Institute (EWRI) of the American Society of Civil Engineers Task Committee on Standardization of Reference Evapotranspiration Calculation, ASCE, Washington, DC 2005.
22. Hargreaves G, Allen R. History and Evaluation of Hargreaves Evapotranspiration Equation. Journal of Irrigation and Drainage Engineering. 2003;129(1):53–63.
23. Wang TL, Campbell EM, O'Neill GA, Aitken SN. Projecting future distributions of ecosystem climate niches: Uncertainties and management applications. For Ecol Manage. 2012;279:128–40. pmid:WOS:000307092900014.
24. Rehfeldt GE, Wykoff WR, Ying CC. Physiologic plasticity, evolution, and impacts of a changing climate on Pinus contorta. Clim Chang. 2001;50(3):355–76.
25. Liu Y, Wang T, EI-Kassaby YA. Contributions of dynamic environmental signals during life-cycle transitions to early life-history traits in lodgepole pine (Pinus contorta Dougl.). Biogeosciences Discuss. 2015;12:14105–38.
26. Fitterer JL, Nelson TA, Coops NC, Wulder MA, Mahony NA. Exploring the ecological processes driving geographical patterns of breeding bird richness in British Columbia, Canada. Ecol Appl. 2013;23(4):888–903. pmid:WOS:000320105100017.
27. Shrestha RR. Modelling spatial and temporal variability of hydrologic impacts of climate change in the Fraser River basin, British Columbia, Canada HYDROLOGIC IMPACTS OF CLIMATE CHANGE IN THE FRASER RIVER BASIN. Hydrol Process. 2012;26(12):1840–60.
28. Hamann A, Roberts DR, Barber QE, Carroll C, Nielsen SE. Velocity of climate change algorithms for guiding conservation and management. Global Change Biology. 2015;21(2):997–1004. pmid:WOS:000348652400040.
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My wife is at work earning money for the family! I have always been the sole provider, but now I have an amazing wife to help carry the load! Go team Sharon!
NEGATIVE: This isn't super clever.
POSITIVE: That's ok. I like to doodle.
Comedy is a weird job where I am my own boss. When 'my boss' tells me to take a nap because I had a late show last night, I feel guilty. My wife is working and I'm taking naps! Not cool!
I never felt guilty about a nap when I was single! That was one of the reasons I became a comedian- so I could nap whenever I wanted!
But now that I'm part of a team, I feel bad about taking naps! It's really hard to sleep when you feel guilty!
I need a time machine so I can take naps in the past when I'm single. I'll send single, energetic, twenty-something Jake to do all my work now. That way, nobody feels guilty about naps, and aimless 26-year-old Jake can feel like he is accomplishing something.


Billy Dean (born William Harold Dean, Jr., April 2, 1962 in Quincy, Florida) is an American country music singer and songwriter, who first gained national attention after appearing on the television talent competition Star Search. Active as a recording artist since 1991, he has recorded a total of eight studio albums, and has charted more than twenty singles on the Billboard Hot Country Songs charts. His biggest solo hits are "Only Here for a Little While", "Somewhere in My Broken Heart", and "If There Hadn't Been You", all reached peaks of #3 on the country music charts.
Billy first became known to fans in the early 1990s with his appearance on "Star Search." He later earned a recording contract with Liberty Records and rose to national stardom with a string of Top 10 hits on Billboard magazine's country singles chart. His debut song, 1991's "Only Here For a Little While," rose into the Top 5, while the follow up, "Somewhere in My Broken Heart," repeated the feat. "Somewhere in My Broken Heart" won the 1992 Academy of Country Music award for Song of the Year; he also took home the ACM's New Male Vocalist award..
Dean's string of hits continued in the early- and mid-1990s, with songs like "You Don't Count the Cost" (1991); "Only the Wind," "Billy the Kid" and "If There Hadn't Been You" (all 1992); "Tryin' to Hide a Fire in the Dark" (1993); "We Just Disagree" (a remake of the 1970s Dave Mason hit) (1993); "It's What I Do" and "That Girl's Been Spying on Me" (both 1996).
In 1991 Dean wrote and sang the theme song to the ABC animated television series Wild West C.O.W.-Boys of Moo Mesa. It is titled "Up on the Mesa".
During the late 1990s, his career slowed down a bit, although he made several television appearances, including "One Life to Live" and "Wings." He and Alison Krauss sang backup on Kenny Rogers' 2000 single "Buy Me a Rose". At the time, the song made Kenny the oldest artist to score a country #1; it also marked the only time that Dean reached the top of the Billboard Hot Country Singles & Tracks chart.
In 2004, Dean was signed to Asylum Curb, where he began a comeback with the single "Thank God I'm a Country Boy" (a remake of John Denver's classic).
In early 2005, he released the album Let Them Be Little, which featured both "Thank God I'm a Country Boy" and the title track. "Let Them Be Little" was co-written by Richie McDonald, who at the time was the lead singer of Lonestar; Lonestar also recorded the song on their 2005 album Let's Be Us Again. Dean's version of the song became his first Top Ten hit on the country charts in nearly nine years.


My takeaways from 3rd lecture of stanford machine learning course.
He starts with explaining probabilistic interpretation with some probability assumptions why least square cost function is a good choice for linear regression problem. Then, at a high level, concept of overfitting and underfitting is described. Then he makes a switch to classification problem and describes why linear regression algorithms discussed in the previous lecture are not good choice for classification problems. After setting up the background, he describes logistic regression algorithm for classification problem. Here, the hypothesis function becomes sigmoid/logistic function instead of linear. And, using the probabilistic interpretation we maximize log likelihood function using gradient ascent(pretty similar to gradient descent) and derive the update rule that looks identical to LMS(least mean square) rule derived in previous lecture. But, its not same because hypothesis is no longer linear but logistic function and take a note that its not a coincidence but there is a deeper reason behind this which will be explained in later lectures when he teaches GLM(Generalized Linear Models).
Then, he takes a digression and explains perceptron learning algorithm(for historical reasons) where hypothesis function is threshold function. This is not considered a good choice because this choice can not be supported with probabilistic interpretations like that in case of previous algorithms.
[This is covered in next lecture actually] Next, Newton's method is explained and then log likelihood is maximized by adapting newton's method by finding parameter $\theta$ where derivative of log likelihood is zero(actually it'll just find the optimal solution as we're not checking the sign of second derivative but in practice it usually maximizes the log likelihood). Usually newton's method converges more quickly than gradient ascent, but involves calculating inverse of hessian matrix of log likelihood function(expensive huh?) and hence good when number of features is small. The generalized version of newton's method in multidimensional setting is called Newton-Raphson method and also Fisher scoring.


Code Sign Error: Provisioning Profile (long string) can't be found.
What am I missing? I looked through related questions and didn't see this scenario already.
In XCode, in the "Groups & Files" pane, expand "Targets" and double-click on your app's target. This brings up the Info pane for the target. In the "Build" section, check the "code signing" section for any old profiles and replace with the correct one.
However, in Xcode 4.2 and later, there is a much easier way to access this text and select and delete it. In the Project Navigator on the left, select your project (the topmost line of the Project Navigator). Now simply choose View > Version Editor > Show Version Editor. This displays your project as text, and you can search for PROVISIONING and delete the troublesome line, right there in the editor pane of Xcode.
Just saw a variation on this issue: I went into the project.pbxproj file as per Brad Smith's notes above, except in this case all of the PROVISIONING_PROFILE lines seemed to be correct, with no occurrence of the "bad" profile string that XCode couldn't find.
However, the fix was the same: deleting ALL of the PROVISIONING_PROFILE lines in project.pbxproj, even though they looked "good" in theory, and then reopening the project in XCode.
In my case the problem was solved by opening Window -> Organizer, selecting my device and removing the old Provisioning Profile under the "Provisioning" panel on the right. The old one was already marked with a red "x" symbol but the iPhone was still using it.
Besides that profile, also the new one was showing up (with the same name) and after simply relaunching the application I had it running smoothly.
Just spent a hour or so doing this and with the help of Brad's advice and a few additional changes it all worked.
I've done this using the following: 10.7.3, Xcode 4.3.2, iOS 5.1 btw.
This should match your provisioning profile in Xcode. To see if they match open Xcode > Window > Organizer > Devices > Provisioning Profiles > Right click on the profile > Reveal in Finder > The filename of the .mobileprovision is your profile id.
8) make sure that the second section matches the first section so that CODE_SIGN_IDENTITY, "CODE_SIGN_IDENTITY[sdk=iphoneos*], and PROVISIONING_PROFILE are all filled in.
One suggestion I'll make since no one yet has said it: PLEASE PLEASE PLEASE make a backup of your whole .xcodeproj file BEFORE you start modifying it's contents. Screwing up the project file and having no backup will lead to a very very unpleasant experience.
Being able to back out of an edit can be a godsend.
I just encountered this problem in my XCode4. To fix it, you need to put all the correct provisions into both Debug and Release config.
I was trying to submit (by archiving) my app. So I just change the Debug provisions to "Don't Code Sign", and the Release provision to my app's appstore provision.
This fix it and enables me to archive normally. Hope that helps.
Select the lines in codesigning that are blank under Any iOS SDK and select the right certificate.
The solution of Brad Smith worked for me, but I also had to remove the CODE_SIGN_IDENTITY field to make it work.
I did roll back to an working version of my project file. As it isn't possible to revert with Xcode (Where is the 'Revert' option in Xcode 4's Source Control?) - I used Tortoise, my Windows machine and this Tutorial (http://tortoisesvn.net/docs/nightly/TortoiseSVN_en/tsvn-howto-rollback.html) to roll back to an older project file.
As the Tutorial didn't work out for me, I just used Tortoise to save the working revision of my project file to an usb stick to port it to my mac. After that I replaced the new broken project file with the old working one, cleaned and it worked like a charm!
Go to both target build settings and project build settings, Under code signing change all the provisioning profiles to something else.
Go to devices/provisioning profiles in the organiser delete the offending profiles, then click the refresh button in the bottom right.
Go back to to the target and project build settings and set your provisioning profiles to the appropriate (hopefully) newly refreshed provisioning profiles.
Under provisioning profile, select the offending provisioning profile and then select a valid provisioning profile in the pull-down menu.
You Could remove old reference of provisioning file. Then after import new provisioning Profile and selecting Xcode builder.
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Early on in the presidential campaign, Donald Trump said he would deport all illegal immigrants and send Mexico the bill. Many thought Trump would back off these claims, but he so far hasn't taken anything back. We calculated the cost of building a wall and deporting immigrants to be a minimum of $330 billion – 237% of Mexico's entire tax revenue. Deporting every undocumented worker would also remove 8.2 million workers from the workforce, which would immediately reduce US GDP by $555 billion and leave a labor shortage of at least 4 million jobs.
Trump has talked tough on immigration policy (and everything else), but come under fire for being vague. To combat that criticism he's now released a policy paper on the topic, which gives more specifics. Among those specifics: make Mexico pay for the wall, triple ICE (Immigration and Customs Enforcement) agents, detain and deport everyone caught trying to cross, and end birthright citizenship (citizenship for children of illegal immigrants for in the country). The paper also suggests cutting back on nearly every worker visa program. It's worth noting that the policy paper only mentions deportation in regards to gang members, but Trump has continued to suggest we deport all illegal immigrants on his campaign trail.
The whole immigration debate raises some interesting data-based questions, and that's where we come in.
How much would it cost to send every undocumented immigrant to Mexico in Trump's 2-year timeframe?
Would Mexico even be able to pay for wall? What about the cost of deportation?
What would the impact on US GDP be if every undocumented immigrant were deported?
We'll handle the first three questions on deportation first, since they're much easier to answer. Firstly, how much would it cost to deport all 11.4 million illegal immigrants? Trump has claimed there might be 30 to 34 million. I don't see any credible data to back this number up, but we'll run the calculations both ways just to see.
Homeland security estimates $8,661 per person for current deportation costs, but this is with present policy and little attempt made at finding people unless they've committed a crime. The Center for American Progress (CAP) estimates an average of $10,070 per person to deport everyone, or $115 billion for all. This is a decent figure at current policy, but if fails to take two major things into account: the increase in personnel needed to deport so many people (a massive cost), and the fact that apprehending illegal immigrants will become increasingly difficult. CAP admits this is a very conservative figure.
We find a more realistic figure from this analysis, which takes into account increased personnel costs and the fact that some immigrants would leave voluntarily. Apprehension costs are still too low to find people who don't want to be found, but we can be conservative and estimate $26,300 per person.
For reference, $300 billion is enough money to pay for everyone going to public college – including room and board.
Unbeknownst to some, we already have a partially-complete border fence along the Mexican border. So far it has cost about $5.3 million per mile. With 1,954 miles of border, a complete fence would cost at least $10 billion total. This is a pedestrian fence; the proposed wall would span the entire border and cost a good deal more. One comparable wall is the Israeli West Bank barrier, which has so far cost about $12 million per mile (274 miles at $3.4 billion). The barrier is mostly fence, but about 10% is concrete. . It also costs over $250 million per year to maintain. Even at these prices the Mexico wall would cost around $23.5 billion. Given that no one has tried to build such a “wall” since the (relatively short) Berlin wall, we looked into the construction of a single meter of fence vs. wall and extrapolated.
A conservative estimate for the final wall would cost at least $30 billion, plus a minimum of $750 million per year in maintenance. $40-$60 billion seems more likely over the difficult California-to-Texas terrain, but in all cases we'll try to use the conservative figure.
In the end, Trump would have to bill Mexico roughly $330.8 billion to cover deportation and wall-building costs. Mexico's total GDP is about $1.26 trillion, and their total tax revenue was about $241 billion in 2014 (19.1% of GDP). Mexico is already struggling to pay the bills and collect taxes – would receive a bill that's more than double all taxes (+237%). Needless to say, this bill would be a huge stretch for a country that has less than 1/5th the GDP per person compared to the United States.
Mexico's leaders have already scoffed at Trump's suggestion, calling it “enormously ignorant.” Even if they were willing to pay the bill it's highly unlikely the country could without collapsing economically and politically.
Estimated GDP is where things get really data-heavy. GDP (Gross domestic product) is the total value of goods and services produced in the country. The United States GDP was about $16.77 trillion in 2013. It doesn't include black market and unreported activities, which are estimated at $2 trillion in the United States. We're going to use the $18.8 trillion estimate that includes the “unofficial economy” or black market.
Pew estimates that the median household income for an illegal immigrant household is about $36,000. Average household size for illegal immigrants is 3.1 persons vs. the national average of 2.4. (Interesting side-note: that means income per capita is about half of the US average). The total income from these 3.68 million households comes out to around $132 billion. Because all of this is in exchange for goods and services, it's part of GDP – less than 1% of the $18.8 trillion.
Income isn't the full story, of course. Comparing total USA income to GDP, we see that Americans make about $6.8 trillion in wages, which is only about 36% of GDP. So if the same ratio held for illegal immigrants we'd see the GDP drop about $365 billion (2%). But both Pew and EPI have calculated that immigrants tend to contribute more to the national GDP, and the illegal immigrant population is more likely to be working (8.1 million of 11.4 million = 71% vs. 63% for all Americans). EPI estimates that immigrants contribute about 14.7% to the overall economy despite making up 13%. If the 29% of those immigrants who are illegal contribute at similar rates, we'd be down $788 billion (4.2%) for 2015.
The right number is probably somewhere in the middle. As it turns out, a more thorough study in California found undocumented workers contribute $130 billion to GDP. About 23% of illegal immigrants live in California, so that number extrapolated to the rest of the nation is $555 billion (3%). This is the best number to use, since it was a direct study of GDP contribution by the undocumented group, and contains the fewest assumptions. 3% might not sound like a lot, but it would erase about 2 years of economic growth (based on 2004-2014).
GDP estimates don't even account for industry shrinkage and losses to global competitors. In reality we'd be looking at a massive shortfall in agriculture, where 6 in 10 workers are undocumented (IPUMS said 60.5%). These jobs pay an a median salary of $9 per hour, and that's the “official” number that businesses report to the government. Farms would either have to cut production or raise labor costs. Whether you increase the cost of production or reduce supply, food costs go up. Increased food prices have always hit the poor hardest, since they spend a greater percentage of their paycheck on food.
Wouldn't Americans fill the jobs?
Some – like the anti-immigration groups Center for Immigration Studies (CIS) and the Heritage Foundation – have argued that jobs would be picked up by American citizens. Let's examine that claim. There are currently 7.9 million unemployed Americans. BLS data includes undocumented workers: the best data shows that about 200,000 undocumented workers are unemployed (lower than the national rate). Illegal immigrants account for about 5% of the total labor force in the United States at 8.1 million undocumented workers. We're already short 400,000 workers, and that's assuming everyone, including engineers and doctors, are willing to take jobs like farm work.
0% unemployment doesn't happen. People move, get fired, or quit; it takes some time to connect with and get hired by a new employer. If we assume the USA's historically low unemployment of 2.5% (3.6 million), every unemployed American's willingness to relocate and to take an immigrant job (regardless of skill level), we'd be short an absolute minimum 4 million workers. So to answer the question: it doesn't even matter if Americans are willing to do the dirty work. There simply aren't enough people.
What about illegal immigrants taking advantage of the system?
There's a lot of bad information out there on illegal immigrants “leeching” off the system. For example, there's a common sentiment that immigrants drain federal resources. The data actually shows that immigrants pay more into healthcare than they take out, pay billions into social security they never claim (most undocumented immigrants ARE paying taxes on payrolls), and don't receive tax credits most low-income American workers receive because they fear deportation. In reality most federal programs require proof of US citizenship.
If you go looking for more data, make sure you understand the context. CIS, for example, riled people up by revealing that illegal immigrant households take an average of $2,700 more in taxes than they pay in, largely through Medicaid. That number (a big bullet point on the top) is meaningless in isolation because it lacks the context of an average low-income American pays in and takes out. If you're not looking closely you'll miss the fact that this is half (65%) the benefits the average American household receives. Adjusting for household size, we see that the average American claims about 280% more than the average illegal immigrant in federal benefits.
Would Trump's immigration policies be a good idea?
Mexico isn't going to be paying the $330 billion bill.
Even if they are, the American economy would take a huge $555 billion hit to GDP.
Without current illegal immigrants, the United States would face a massive labor shortage in low-income jobs, especially agriculture.
With lower demand or higher supply-side costs, food prices would rise, which would hurt low-income Americans most.
Did we miss something? Forget to add something to the totals? Let us know what you think about Trump's immigration policies.
Ryan Whitacker is a data freak, Excel ninja, and open data fanatic. He combines these skills to put together analysis in hopes it will help you make a better decision.


In Indonesian, is "gloves" plural in this context? Would a better English translation be "Coat and gloves"?
The correct translation for this should be "Jacket/blazer and gloves."
Coat is "mantel", a different heaviness to it as opposed to a jacket which is typically lighter. In natural conversation if you were speaking of just one glove, that would be pointed out, so the correct translation should be "gloves".
Is there a lot of call for jacket and glove(s) in Indonesia?
And didn't the lesson begin with teaching that "coat" is /mantel/. Coat...jacket... random!


(INTELLIHUB) — Former President Barack Obama lost 2 million of his fake bought and paid for Twitter followers on Thursday with the social media giant’s recent crackdown on fake account.
At press time, the Twitter purge had cost Obama 2,346,119 followers, or more than two percent of his audience on the site. With more than 101 million remaining followers, Obama remains the third most-followed person on the site, a few million followers behind singers Katy Perry and Justin Bieber.
Other major political accounts were also hit by the purge, with President Trump losing 325,038 followers so far, a 0.7-percent decrease. As the first day of the Twitter purge wrapped up, Trump had more than 53 million followers left.


Kemper sells Medicare Supplement insurance through one of the companies in their group, Reserve National. They are based out of Oklahoma and operates independently as a subsidiary under the Kemper name.
Founded in 1956 by a former insurance agent, Reserve National was founded in Oklahoma but eventually expanded to a total of 38 states. In 1998 Reserve National was acquired by Kemper, a large group of insurance and financial services companies operating nationwide.
Reserve National uses the Kemper name and branding, although they operate as an independent company. They offer a long list of supplemental health insurance products as well as a handful of life insurance policies. Among their supplemental insurance plans is Medicare Supplement, and it is to Reserve National that Kemper sends all of their Medicare business.
Reserve National’s Medicare supplement plans are sold throughout the country by independent local agents. The company’s headquarters is located in Oklahoma City.
The Reserve National website offers very little information on their Medicare Supplement products. There is a basic overview of what Medicare Supplement insurance is for, the site notes that they have preferred rates for non-tobacco users.
Most states use a letter-coded standardized system for Medicare Supplement, and insurance companies must use this system as well. Since all companies are required to carry Plan A and either Plan C or F, we can safely say Reserve National offers the first and one or the other of the second and third. Beyond that, we cannot guess as to which of the total off eleven plans they write.
Plan A is the base plan, and Plan F is the highest-level plan available. Most companies offer both of these as well as a few other plans.
Online quoting is not offered by Reserve National, nor is there a rate chart such as that offered by many other companies.
To obtain a rate, there is a request form that can be filled out in order for a representative to contact you, or you can use the agent locater option to get rates from a local agent directly.
Given the lack of information in general on the Reserve National site, it is not surprising to see that there is no information with regards to their claims process. Most Medicare Supplement plans offer direct claims handling with providers, so the lack of this information is fairly standard.
There are some claims forms on the website, but nothing specifically related to Medicare Supplement.
Reserve National has an A+ rating with the Better Business Bureau (BBB), with a total of seven complaints on file in the past three years. They have been accredited since 1965.
There are two negative reviews of this company on Yelp. Neither of these complaints is related to Medicare Supplement, however.
We did not find other complaints anywhere directly relating to Medicare Supplement, and very little overall in the way of information on this company.
Kemper/Reserve National offers very little information on their products, and without rates to compare it is difficult to form an opinion on their Medicare Supplement offerings or service. There are no red flags in terms of their reputation, but again the general lack of information makes it hard to draw conclusions.


Was Churchill a great orator?
Throughout his career Churchill witnessed some extraordinary developments on the world and national stage and, as a politician and statesman, he had opinions on many of them. He has a reputation as one of the greatest orators of the twentieth century. But there are a number of factors that must be considered when assessing whether Churchill was a great orator and investigating whether the legacy left by his famous speeches is truly deserved.
It is certainly true that Churchill worked very hard at his oratory. As a young man he struggled to overcome a speech impediment and throughout his life he had difficulty pronouncing the letter ‘s’. Churchill also always prepared what he wanted to say very thoroughly and spent hours polishing and reworking his speeches. In the early part of this life he learnt his speeches by heart and relied on his memory. Churchill’s speeches at the time would have been heard by those in the hall where he was speaking and then recorded either in newspaper reports or in Hansard, the edited report of proceedings of both the House of Commons and the House of Lords. In April 1904 Churchill lost the thread of what he was saying and had to sit down rather than completing his speech. From this point on, he made sure he always had complete notes for his speeches and those speech notes are preserved in his archive. The final versions of his speech notes were often even laid out on the page like poetry – a technique to make sure that he did not lose his place.
Around 1897 (before Churchill had much experience of speaking in public), he wrote an article entitled ‘The Scaffolding of Rhetoric’ (CHAR 8/13/1-13) where he set out his thoughts on what made a good speech. Even though the article was not published, we shall use this as the basis of our assessment of Churchill as an orator.
The prime minister of the wartime Coalition government Sir Winston Leonard Spencer Churchill, (1874 - 1965), during a speech. The July 1945 general election resulted in a resounding victory for the Labour Party.
When Churchill entered politics in 1901, there were no audio recordings so we cannot listen to or experience them for ourselves. So our appreciation of the impact of his skills as an orator at that time comes from those who wrote to him about his speeches or from newspaper articles. However, when looking at these sources we must weigh up information about the context. For example, when Lord Cromer congratulated Churchill on his maiden speech in February 1901 (CHAR 1/29/8) it may be appropriate to consider who Cromer was and think about what motivated him to write. Perhaps Cromer may have known Churchill’s parents; or as a colonialist, he may have seen Churchill as a kindred spirit.
Similarly, the evidence for Churchill’s first political speech in 1897 is a newspaper report of it (CHAR 2/21/1). However, again we must assess not just the content of the speech but also what we know about its audience. The speech was made to the Bath branch of the Primrose League, an organization dedicated to promoting Conservatism and founded by Churchill’s father. So we can deduce that Churchill would have been likely to have a receptive audience when he talked up the Conservatives (even though his speech was directed in part at the Workmen’s Compensation Bill). Churchill also thought about making comments which would hit home with his audience. He talked about Lord Beaconsfield (Benjamin Disraeli) whose love of primroses had given the Primrose League their name – an allusion he may not have made in other company. Despite his message being radical at times (for example he ‘hoped that the labourer would become a shareholder in the profits’) the speech was ‘well received’ and this may be an early indication of Churchill’s skill as an orator.
As the twentieth century progressed, speech making become very important. New technologies, particularly radio, allowed politicians a means of communication with a mass audience. This was important for politicians in democracies and dictatorships. During the Second World War, Churchill’s skillfully crafted speeches had a massive impact. They were designed not only to restore British morale, but also to send a message of hope to occupied Europe, a signal of defiance to Nazi Germany, and an appeal for support to the United States. Churchill’s political opponent, Clement Attlee, summed it up: when asked what Churchill did to win the war he replied: 'Talk about it'.
Churchill’s command of the English language was considered second to none and many ordinary people listening to him felt that he spoke to them personally. Churchill’s reputation as an orator was also cemented by his ability to craft memorable phrases which have passed into history – phases such as ‘Never in the field of human conflict was so much owed by so many by so few’ (CHAR 9/140A/9-28).
Churchill was one among several fine orators of the twentieth century – we think about Hitler, Mussolini, F.D. Roosevelt, Martin Luther King, Nelson Mandela and others. Today we can not only hear but also see our politicians delivering their speeches in Parliament and judge for ourselves whether they are fine orators or not.


A month ago, when news of a potential plea deal for former Democratic House staffer Imran Awan began to bubble up, President Trump took to Twitter to cry foul.
That same Justice Department's response today? To issue a subtle but stinging rebuke of Trump and his cohorts who pushed conspiracy theories about Awan.
Particularly, the Government has found no evidence that your client illegally removed House data from the House network or from House Members’ offices, stole the House Democratic Caucus Server, stole or destroyed House information technology equipment, or improperly accessed or transferred government information, including classified or sensitive information.
Each of those accusations can be linked to conspiratorial reporting and suggestions about the actions of Awan, a Pakistani American whom Trump once suggestively labeled the “Pakistani mystery man.” They do not directly deal with any criminal charges lodged against Awan, but prosecutors put them in there anyway — clearly wanting to dispatch with their own president's and his media allies' conspiratorial ideas, for good measure. The section could be read as more a rebuke of conservative media outlets than of Trump, but Trump was barking up the very same trees.
To be clear, the paragraph comes after an 18-month investigation undertaken not just by Trump's Justice Department but also by his own nominee in U.S. attorney Jessie K. Liu. As part of the deal, Awan pleaded guilty to making a false statement on a bank loan application, a charge for which prosecutors will not recommend jail time.
About a year ago, Trump began hinting at potentially treasonous actions and a broader scandal involving Awan. Conservative outlets had been on the case for months, fueled by Awan's ties to Rep. Debbie Wasserman Schultz (D-Fla.), a preeminent GOP bogeywoman who used to run the Democratic National Committee. When Awan was arrested just before boarding a plane for Pakistan, the intrigue only grew — even though he was charged not with conspiring, but with bank fraud.
The whole thing fit neatly into Trump's conspiratorial, deep-state allegations about the Russia investigation, in addition to his clear doubts about his own intelligence community's explanations about the DNC hack during the 2016 election.
Trump is unlikely to take his medicine and admit he oversold the prospect of an Awan scandal. Indeed, one of the features of conspiracy theories is that every rebuke just becomes the latest evidence of a supposed coverup. The report's release coincided with Trump lodging an entirely new technology-related conspiracy theory — this one about the National Security Agency deleting 685 million phone calls and text messages that were obtained from telecommunications companies as part of various investigations.
If anything, it shows Trump is happy to keep lodging accusations at his own government, regardless of how flimsy the facts that undergird them are and that they may one day be revealed as nonsense.
It also shows those around him — and even those he has appointed — increasingly feel the need to do something about it.


Project Prevention: Eugenics and Social Engineering?
Three hundred bucks can buy a lot of heroin, which makes Project Prevention's legality questionable.
In the early 20th century, the eugenics movement took root in the U.S. Eugenics is the concept of that a "fitter" human species can be created by removing those with undesirable traits -- like congenital or chronic diseases or low socioeconomic status -- from the gene pool. For several decades, eugenics enjoyed popular support. In fact, from the 1910s to the 1970s, federal and state governments carried out the sterilization of more than 60,000 Americans who suffered from epilepsy, mental illness, blindness or homelessness. The number also included healthy people of color [source: Lombardo].
Some allege that CRACK is simply the newest incarnation of this type of social engineering.
"It's simply a bribe for sterilization," said one Planned Parenthood director in 1999 [source: Belluck]. Indeed, that organization is one of numerous voices that allege Project Prevention's goals are immoral, unethical and possibly illegal -- at least as far as contract law goes.
Legally, the issue is whether a drug-addicted person is legitimately of sound mind and hence in a capacity to make the life-altering decision of sterilization. What's more, the concept of informed consent -- a custom associated with sterilization requires that the patient choose the procedure freely. Critics of Project Prevention argue the $300 cash given to the addicts is decidedly coercive [source: Scully].
The group is also often criticized as unethical for what appears to be a racial bias in Project Prevention. CRACK points out that under 1,000 of their clients have been black, whereas more than 1,800 have been white. However, critics point out that whites make up 79 percent of the U.S. population, while blacks account for just about 13 percent [source: U.S. Census Bureau, Newman]. In terms of hard numbers, the program appears equitably distributed by race, but is skewed disproportionately toward blacks.
Beyond race, the very design of the program is classist by nature. One critic describes CRACK as "a structure in which the economically privileged can and do dictate who will and who won't have children" [source: Scully]. Those in the drug treatment field criticize the narrow focus of the program; addicts aren't offered parenting or drug counseling, only sterilization. What's more, as critics point out, they're likely to use the money to simply buy more drugs.
For her part, Barbara Harris says she doesn't understand the controversy her program generates. She is the adopted mother of four children born to addicted parents. The people eligible to participate in her program have already borne at least one child already. And, ultimately, the decision to be sterilized has been left up to the addicts by the courts.


One of the big challenges in astronomy is determining cosmic distances. There are lots of ways we can do this, including things like Type Ia supernovae, brightness changing stars called Cepheid variables, parallax, etc. Each method has advantages and disadvantages, and each has certain inherent uncertainties. To minimize these uncertainties, we try to find objects we can measure in multiple ways. By comparing different methods, we gain a better understanding of the bias of each method so that we can account for them. In this way, we can build a “cosmic distance ladder” using different methods at different scales. So even though we can only use supernovae to determine the distance of galaxies billions of light years away, we know our measurements are accurate because our cosmic ladder gives us confidence in its accuracy.
Of course we always want to make our scale more accurate. For this task, finding an accurate measurement of the distance to the Large Magellanic Cloud (LMC) is always useful. The LMC is a satellite galaxy of our Milky Way. It is about 163,000 light years away, which is distant enough to test large-scale methods like supernovae, but close enough that we can use more accurate small-scale methods. The LMC is crucial to making our cosmic distance ladder more accurate.
Earlier this year, a new measurement of LMC distance was published in Nature (doi:10.1038/nature11878). The team obtained this measurement by observing eclipsing binary stars in the LMC itself. The eclipsing binary method basically works by taking two measurements of a binary system. The first is to measure the Doppler shift of the light from each star. Since light from a star is shifted to the red or blue as the star moves away from us or toward us, measuring the Doppler shift tells us how fast the stars are moving relative to each other. The second measurement is to observe the brightness of the binary system. Since the system is an eclipsing binary, there are times when one star passes in front of the other. When this happens the system dims a bit since one star is (at least partially) blocking the other. By measuring these dips in brightness you can compare the brightness of the two stars.
These two measurements allow us to determine the mass of each star, from which we can gauge the absolute magnitude (real brightness) of the stars. By comparing them with their apparent magnitude (apparent brightness) we can determine their distance, since the more distant and object, the light bright it appears. This method has been used before, but only with young bright stars, typically with short orbital periods. The downside of young stars is that their brightness can vary a bit. Also, if the stars are relatively close, the gravitational forces between them can affect their brightness. So there has always been an uncertainty of about 5%-10% in this method.
What this team did was to go through data from another project that has been observing the LMC for about 16 years. The original project was looking for gravitational lenses, but the data is available for others to use. So this team went through 16 years of observational data and found eight eclipsing binaries that were both cool, older stars, and that had long periods (on the order of 100s of days). You can see these eight binaries indicated on the image below. Using these binaries the team was able to measure the LMC distance to within 3% accuracy. Their result is a distance of 162,980 light years.
While a more accurate distance measurement is always good, this work also demonstrates the power of sharing data. The original gravitational lensing project had nothing to do with eclipsing binaries. By making the data freely available, others were able use it in new ways. It’s this type of collaboration and openness that can make science truly powerful.


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Given last year's events, it should come as no surprise that digital rights dominate my 2002 'You Can't Be Serious' awards. These annual awards recognise truly astonishing achievements in IT double-think.
Given last year's events, it should come as no surprise that digital rights dominate my 2002 "You Can't Be Serious" awards. These annual awards recognise truly astonishing achievements in IT double-think.
In that spirit, the "Beverage Through the Nose" award for 2002 goes to US Congressman Howard Berman.
here. In one amusing argument, he suggests that companies should be held blameless for breaking into your network to search for copyrighted content. It seems he came up with this theory because people are already given the leeway to trespass on someone's lawn to retrieve a stolen bicycle that's in plain view. To be sure, Richard Nixon's infamous Checkers speech was more inspired, but Berman's works of lunatic reasoning show great promise.
Microsoft ranked a close second in this category, but I have no pity for the poor fool with a mouthful of ginger ale when he heard Bill Gates first utter the words Trustworthy Computing last January. After hearing terms like Zero Administration Windows, everyone should know by now that it's dangerous to drink anything carbonated during an announcement from Redmond.
Nevertheless, Microsoft does run away with three major awards this year. The first is the "Information at Your Fingertips" award. How many times has this happened to you? You're at a company party shooting the breeze with your boss about the clever plot of a DVD movie you rented and watched yesterday. But when she asks about the name of the movie, your mind goes blank. Until recently, you'd have to wait until 3am for the name to come back to you.
Now the information can be as close as your cellphone. Every time you use the Windows Media Player to watch a new DVD, it automatically identifies the DVD to a Microsoft server, along with a unique identifier for your personal player.
So the next time you're on the spot for the name of some DVD you watched, give Microsoft a jingle and ask. Someone at the company should know the answer.
Naturally, this convenience will be unavailable to you if you don't use Windows Media Player. Fortunately, Microsoft devised an ingenious plan to prevent you from accidentally using less capable alternatives.
Here's how it works: along with automatic updates to software and your end-user agreement, Windows XP periodically downloads the information it needs to prevent unapproved media players from accessing DRM-enabled content.
As DRM, or digital rights management, gains momentum and all your other software stops working, Windows Media Player is sure to become the irresistible choice for all your entertainment needs.
This ability to assert remote control over your software earns the company the "Zero Degrees of Separation" award for 2002. Granted, you could miss out on features like this if you don't have a PC, but it's safe to assume that Microsoft has similar plans for the Xbox and that companies such as Disney and Sony are hard at work figuring out how to control your access to copyrighted media on standard players. Better watch out, Microsoft, or one of these other companies could nudge you out of first place in this category next year.
The third award for Microsoft is the coveted "Ignorance Is Bliss" prize for 2002. The fact that sane people still use Windows or Media Player testifies to how well the company has been able to protect its customers from information about what these products are doing without their informed consent.
Finally, Ximian walks away with "The Mouse That Squeaked" award for continuing to reproduce the .Net development framework as an open source project called Mono. The value of Mono eludes me, but perhaps there's a secret contingent of open source programmers itching to write code in Visual Basic .Net.
Nevertheless, only delusions of grandeur could account for the notion that Microsoft won't bankrupt Ximian and stop the project on claims of patent violations the moment Mono poses a threat.


Germany's local governments have called for more help from their federal counterparts to integrate refugees into the job market.
Nearly 600,000 refugees were claiming unemployment benefits in Germany in mid-2017, some 250,000 more than at the same point in 2016, according to Gerd Landsberg, head of the German Association of Towns and Municipalities (DStGB). Just under 200,000 refugees, meanwhile, had found jobs and had begun to pay social security contributions.
"The figures show that we still have massive efforts to make until we have integrated refugees into the labor market," Landsberg told the Funke Media Group, before adding that Germany was stuck in "fixed integration patterns."
Landsberg's comments were backed up by Vice Chancellor and Foreign Minister Sigmar Gabriel, who in an interview last week also said that local governments deserved more money to help integrate refugees. "We have to reward the towns and municipalities for taking in refugees," he told the Funke group on Saturday.
"They should get the cost of integration replaced by the federal government," he said. "And they should get the same amount on top for their citizens."
The cost of housing and administering refugees caused much friction in German communities at the height of the refugee influx in late 2015 and early 2016, especially in urban areas like Berlin, where local authorities were often short of cash, and municipal buildings like sports halls were requisitioned as emergency housing for new arrivals.
At the moment, Germany actually has the opposite problem. The closure of the so-called Balkan route through Europe (the borders between Serbia, Hungary and Austria are all now effectively shut to migrants) means that the number of new refugees dropped sharply in 2017.
As a result, a survey by public broadcaster WDR found earlier this monththat a third of all refugee shelters in the state of North Rhine-Westphalia remained empty, with the costs of maintaining the shelters running into tens of millions of euros.
Gabriel, former leader of the Social Democratic Party (SPD), said it was crucial that local authorities weren't faced with a decision between housing refugees and, for instance, renovating a public swimming pool. "On this basis the municipalities should decide for themselves how many refugees they take in," he said. "That way we can prevent people getting the impression that everything is being done for refugees and nothing for them."
European solution — or Danish model?
Gabriel also suggested that the European Union could step in to organize a continent-wide plan to promote refugee integration. "The EU could implement a program to help finance municipalities in poorer member states," he said — in other words, countries would be rewarded for taking in more refugees.
Figures revealed last week showed that the agency in charge of administering Germany's refugee policy, the Federal Office for Migration and Refugees (BAMF), would miss its own integration targets this year. The waiting time for migrants to start integration courses rose to 12.5 weeks by the end of November, well short of the six-week goal BAMF had set for itself. In January, the wait time for an integration course was 10.9 weeks.
Not only that, BAMF also missed a goal of having 430,000 people participate in integration courses this year. Up to the middle of December, only 280,000 participants had taken part in integration courses in Germany.
According to Landsberg of the DStGB, this is in stark contrast to Denmark, where refugees were being integrated into the job market even while they were attending Danish language classes.
Landsberg also said that major companies could do more to get refugees jobs in Germany, arguing that training and qualification for new arrivals was in the interests of German businesses as well.
Germany's empty refugee shelters: Sensible backup, or waste of money?


The federal government has celebrated the release of the last children from Australia's mainland immigration detention centres, despite lingering questions over how much freedom some families have been awarded.
On Sunday, Immigration Minister Peter Dutton said he felt "a great sense of achievement" at having moved into the community the final 43 children who were held in onshore detention at the end of February. About 50 children also remain in detention on Nauru.
He rejected a report published by Guardian Australia suggesting the government had simply reclassified sections of detention centres as "community detention" in order to make the claim.
"The same definitions apply today as they did before," Mr Dutton said. "There are certain characteristics that need to be met in relation to all these definitions, but that's all beltway stuff."
Pushed on the question of whether the children were really out of detention, he told reporters: "They're outside of 'held detention', so that's the answer that I've provided to you before."
Fairfax Media understands that in at least one case, a family remains in immigration-controlled residential housing near the Villawood detention centre in Sydney, but they have been granted free movement. It is understood the children's father remains detained at Villawood due to an adverse security finding by ASIO.
In respect of those circumstances, a spokeswoman for the minister told Fairfax Media: "There are arrangements that have been put in place. Those arrangements now sit with the fact that it's community detention."
Mr Dutton's office did not respond to requests for further clarification on Sunday. On Monday morning the minister told ABC radio the special arrangements described above only involved one family, located at Villawood.
Before the 2013 election, the number of incarcerated children peaked at close to 2000, in what the government argues is the shameful legacy of Labor's failed border protection policies.
Those families who have been released from "held detention" are now in community detention, which means they reside in community housing at a specified address, but are generally free from physical supervision. Legally, they remain in immigration detention and the responsibility of the minister.
Australian Human Rights Commission President Gillian Triggs said she "warmly" welcomed the announcement, while Prime Minister Malcolm Turnbull said it was the realisation of a long-held government priority.
"The real message here is that we have stopped the boats," he said.
But the threat of return to Nauru still hangs over about 150 people, including children and their families, who were transferred to Australia for medical treatment. And 50 minors remain in Australia's offshore detention facility on Nauru, in conditions the United Nations has described as "harsh" and short of international standards. There are no children in detention on Manus Island, according to the most recent department statistics.
Refugee Action Coalition spokesman Ian Rintoul welcomed the development but described it as a "really qualified shift" from the government.
"There's a bit of administrative sleight of hand," he said. "The position of people who remain in community detention is no clearer."
Mr Rintoul said he still had "enormous concerns" about the future of families in community detention, particularly those who could be returned to Nauru.
Greens senator Sarah Hanson-Young called on Mr Turnbull to rule out returning any children to Nauru and to close the detention centre on the island nation.
"Clearly, Malcolm Turnbull is seeing that the Australian public don't want to see children being used in this political point-scoring game on who can be toughest on refugees," she said. "In order to pay proper credit to that, he must close Nauru and bring all of those families and those hundreds of children here to Australia."


My father in law has an X/19 that he is losing faith in. Despite lots replacing obvious and not-so obvious parts it will not restart after a decent run until its cooled down again.
Has anyone experienced this before or have any idea what the cause might be?
It's probably fuel evaporation. The fuel chamber in the carb sits directly above the exhaust manifold. You should have a spacer under the carb and over stretching the exhaust. It's a typically clumsy fiat design and far from ideal. There is a fan designed to blow air on the chamber via a paper tube. These are often removed though as they're a drain on the battery. Sometimes the tube is missing too.
Does yours have a fan? Does it work?
I used to run on K&N filters so the pancake filter housing can be removed. This allowed better heat dissipation and stopped the hot start issues.
There are several things you can do to solve it, an electric fuel pump will help but won't stop fuel evaporating!
There are a number of facets to this problem.
Fuel evaporation is just one of them but is quite common.
Next on the list is the earthing of the starter motor - if the battery "feels" flat when the engine is hot and won't turn the engine it is worth getting the earthing and main power feeds checked (try looking up the brown wire mod for some help along these lines).
If the earths are good but the engine just won't turn then the odds are you have one of the engine blocks that are slightly distorted (usually the result of overheating earlier in the car's life). Sadly the only option is a different engine block as the main shell bearings are no longer in alignment when the engine is hot. It saps power when it is running too so could actually save money with a fix in the long run due to better fuel economy.
If the engine turns but won't catch then you most likely have a fuelling or ignition problem. The electric fuel pump and electronic ignition mods will go a long way to help. Swapping the original air filter housing for a smaller chrome or plastic filter/housing goes a long, long way to helping - as does opening up some of the slats in the engine cover (the inner side of the cover and not directly over the block). Again you can find more information on this if you do a search.
The final significant possibility is a loss of compression while the engine is hot. If it does turn freely it is worth getting a compression test done as a precaution. It is not unusual for the 2 and 3 cylinders to cross-leak if there is any distortion in the head but in the early stages it only shows up while the engine is properly hot and that normally happens only after the engine is shutdown. Later engines have additional headbolts to address the issue at least in part.
An electric fuel pump solved the issue for me too, glad to hear its sorted!
anyone care to share details of the electric pump mod please?