Morning HotCoffee,

Interesting that all the vote counting is over and Brenda Snipes was unable to find enough extra voters to get her candidates to the finish line. The leftie/socialist in GA is another story. You will find this interesting. A great example how leftie/socialists twist numbers to suit their screed.

"Democrats Claim The GA Governor’s Race Was Stolen From Stacey Abrams. One Fact-Checker Completely Destroys That Theory."

Catch you later.

KEY RESOURCES FROM THE IPCC:

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Creation of the IPCC Data Distribution Centre

The DDC has been established to facilitate the timely distribution of a consistent set of up-to-date scenarios of changes in climate and related environmental and socio-economic factors for use in climate impacts assessments. The intention is that these new assessments can feed into the review process of the IPCC.

The initiative to establish a DDC grew out of a recommendation by the IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis (TGICA). This Task Group was itself formed following a recommendation made at the IPCC Workshop on Regional Climate Change Projections for Impact Assessment (London, 24-26 September 1996).

The establishment of the DDC was approved by the IPCC Bureau at its Thirteenth session (9-11 July 1997) and it was subsequently determined at the XIIIth IPCC Plenary (Maldives, 22-28 September 1997) that the DDC would be a shared operation between the Climatic Research Unit (CRU) in the United Kingdom and the Deutsches Klimarechenzentrum (DKRZ) in Germany. In 2003 a third centre, the Center for International Earth Science Information Network (CIESIN) in the USA, joined the DDC collaboration. From February 1st, 2007 British Atmospheric Data Centre (BADC) has replaced CRU as the United Kingdom partner.

TGICA Mandate and DDC Governance

The conduct of TGICA is governed by its mandate, which can be downloaded here as an Adbobe pdf document. The TGICA has established a set of procedures for managing content on the DDC as described in the DDC Governance document, which can be downloaded here as an Adbobe pdf document.The conduct of TGICA is governed by its mandate, which can be downloaded here as an Adbobe pdf document. The TGICA has established a set of procedures for managing content on the DDC as described in the DDC Governance document, which can be downloaded here as an Adbobe pdf document.

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The observational record:

The IPCC Data Distribution Centre provides access to observed data covering the physical climate (e.g. global distributions temperature and rainfall), atmospheric composition, socio-economic information (e.g. national population and income data), and impacts of climate change. Use the links below to reach these 4 sections:

• Climate System Scenario Tables (Annex II of IPCC 5th Assessment Report, WG1 -- as Excel workbook
• Observations of the climate
  • Global mean temperature data used in the AR4 assessment report;
  • High resolution climatology prepared by the Climatic Research Unit (CRU) of the University of East Anglia (UEA).
  • Previous version of CRU data preserved for reference.
• Carbon Dioxide: observed atmospheric concentrations
• Socio-Economic Baseline Dataset
• Observed Climate Change Impacts:
  • IPCC AR5 Observed Climate Change Impacts
  • IPCC AR4 Observed Climate Change Impacts
• Environmental Data and Scenarios

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PHYSICAL CLIMATE

Data available thorugh the DDC

• Data used in the AR4 assessment report: four datasets were used in the report to evaluate trends over the past century. These are available either as global and hemispheric aggregates or as low resolution (5 degrees by 5 degrees) spatial fields.
• High resolution climatology: a high resolution data set prepared by the Climatic Research Unit (CRU). This dataset is not, however, considered to have the same reliability for studies of long term trends as the low resolution CRU dataset which was included in the AR4 assessment (see above). This dataset is available in NetCDF and GeoTIFF (GIS compatible) format.
• Previous version of CRU data: these data have been superceded by the new versions above, but are preserved in the archive.

Links to other Public-Domain Observed Climate Datasets:

A list of external climatological datasets is also included in the DDC. The list is not comprehensive, and is continually being updated.

• Global Historic Climatology Network (GHCN) monthly data
• International Research Institute for Climate Prediction/Lamont-Doherty Earth Observation at University of Columbia
• NCEP Re-analysis Data
• British Atmospheric Data Centre (BADC)
• Global Precipitation Climatology Centre (GPCC)
• National Centre for Atmospheric Research (NCAR) Data Support System
• Climatic Research Unit (CRU) data
• World Data Center - A. Meteorology
• Climate Diagnostics Centre at NOAA
• Comprehensive Ocean-Atmosphere Data Set (COADS) at NOAA

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Carbon Dioxide: Projected emissions and concentrations

Storylines and emissions

The greenhouse gas forcing used by the AR4 models are derived from SRES emissions scenarios. The SRES report discusses emissions projections produced by a range of Integrated Assessment Models for a range of socio-economic storylines. Four `marker scenarios' are recommended as the basis of climate model projections, together with two further `illustrative scenarios':

Storyline Integrated Assessment Model Status
A1 (A1B) AIM Marker
A2 Marker
B1 IMAGE Marker
B2 MESSAGE Marker
A1T MESSAGE Illustrative
A1FI MINICAM (A1G) Illustrative

The projected emissions for the marker scenarios and other scenarios discussed in the SRES report are available here. Note that the A1FI set of scenarios was formed as a union of the A1G and A1C scenario groups: in some documents the A1FI MINICAM illustrative scenario is referred to as the A1G MINICAM scenario.

Recent increase and projected changes in CO2 concentrations

The modern increase in atmospheric CO2 concentration is shown in the Manua Loa time series in figue 1 below. The annual means are plotted, and can be downloaded (here). More data and information on CO2 monitoring is available from the NOAA Earth System Research Laboratory Global Monitoring Division, including monthly data from Mauna Loa.

For each of the illustrative and marker emissions scenarios, CO2 concentration projections calculated by two different carbon cycle models were reported in IPCC (2001) and used as the bases for climate model projections there and in the Fourth Assessment Report. Thes are also shown in Figure 1. The carbon cycle models, ISAM and BERN, are described in Box 3.7 of IPCC (2001). The projected concentrations are given in Annex II of the IPCC Third Assessment WG I report (the `reference' projections were used). The data can be obtained as plain text files here: ISAM, BERN.

[image - graph - Figure 1: Atmospheric CO2 concentrations as observed at Mauna Loa from 1958 to 2008 (black dashed line) and projected under the 6 SRES marker and illustrative scenarios. Two carbon cycle models (see Box 3.7 in IPCC, 2001) are used for each scenario: BERN (solid lines) and ISAM (dashed).]

As shown in figure 2, atmospheric CO2 concentrations since 1990 have risen in line with projections from the SRES report, lying close to the highest of illustrative/marker scenarios predicted with the BERN model, and towards the lower limit of the ISAM illustrative/marker scenario projections. The ESRL "annual average marine surface air CO2 concentrations" shown in figure 2 is of shorter duration (starting in 1980), but gives an accurate estimate of the global mean CO2 concentration. The concentrations at Mauna Loa are generally 1 to 2 ppmv lower that the ESRL values. In the time period shown in figure 2 the difference between the carbon cycle models is comparable to the difference between the two extremes of the illustrative/marker scenarios, but later in the century (figure 1) the difference between scenarios dominates. Uncertainties in CO2 concentrations due to the range of possible climate - carbon cycle feedbacks in the BERN and ISAM models are larger than the differences between these two models.

[image - graph - Figure 2: Observed and projected atmospheric CO2 concentrations since 1990. Triangles show annual average marine surface air CO2 concentrations for the period 1990 to 2008 downloaded from the NOAA ESRL web site (www.esrl.noaa.gov) in September 2009. The red and green lines shows annual average CO2 concentrations for the SRES A1B (red) and B2 (green) marker emissions scenarios projected using the reference version of the Bern carbon cycle model (solid) and the ISAM model (dashed). These two scenarios give the upper and lower limits of the 6 illustrative/marker scenarios in the period plotted. The blue shading shows the range from low to high climate - carbon cycle feedbacks. All SRES projections are taken from Annex B of the 2001 IPCC Working Group I Report.]

Carbon cycle models and GCMs

The carbon cycle model used to generate the forcings used by various AR4 General Circulation Models are listed in the table below:

Carbon Cycle ModelGeneral Circulation ModelISAMMRI:CGCM2_3_2; NASA:GISS-AOM, GISS-EH, GISS-ER GFDL:CM2 GFDL:CM2_1BERN-CC

BCC:CM1; BCCR:BCM2; CNRM:CM3;
INM:CM3; IPSL:CM4; LASG:FGOALS-G1_0; NCAR:CCSM3;
NIES:MIROC3_2-HI, MIROC3_2-MED;
UKMO:HADCM3, HADGEM1

Changes in CO2 in the distant past

The observed and projected changes in CO2 concentrations can be put into context by comparing them with measurements of past variations. The levels of carbon dioxide in the atmosphere in the distant past can be determined from bubbles of air trapped in ice. Data from the Antarctic sites Vostok and Epica ice cores (see Fig. 6.3 of the IPCC AR4, the physical science basis are plotted together in figure 3. Also shown are the AD2007 annual mean concentration measured at Mauna Loa and the the projected concentrations for AD2100 under 6 SRES marker and illustrative scenarios. The 50 year period of Mauna Loa observations and the century covered by the projections span less than the thickness of a line on this graph.

[image - graph - Figure 3: CO2 concentrations derived from EPICA and Vostok ice cores. The red bar at the side indicates the evolution of the Mauna Loa measurements. On this time scale, the 50 years of measurements span less than the thickness of the line, so it appears vertical.]

Content last modified: 04 April 2014

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Socio-Economic Baseline Dataset

An evaluation of climate change impact requires the establishment of some baseline period against which changes will be measured. Baseline data are required for the relevant climate variables and also for non-climatic information (for example, carbon dioxide concentration, soil characteristics, population, income levels, etc.). Ideally, these various baseline datasets should all refer to the same time period, whether 1961-90 averages or the 1990 value. We provide here a set of country and regional-level indicators of socioeconomic and resource variables as estimated at the beginning of the 1990s. These data are reproduced from the IPCC report on The Regional Impacts of Climate Change: An Assessment of Vulnerability published in 1998 by Cambridge University Press (Appendix D). These data were collated from a variety of sources such as the World Bank, the United Nations Environment Programme (UNEP) and the Food and Agriculture Organization of the United Nations (FAO).

These baseline data have been organized into nine major world regions: Africa, Australasia, Europe, Latin America, Middle East/Arid Asia, North America, Small Island States, Temperate Asia, and Tropical Asia. They can be viewed (and downloaded) following the links below.

Data Tables

Data tables in HTML-format
Data tables in Excel-format

Reference:

R. T. Watson, M. C. Zinyowera and R. H. Moss (Eds), "The Regional Impacts of Climate Chamge: An Assessment of Vulnerability", 1998, Cambridge University Press, UK.
Available online at: http://www.grida.no/climate/ipcc/regional/index.htm

Content last modified: 14 May 2018

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IPCC AR5 Observed Climate Change Impacts

Recent warming around the world has caused changes in many physical and biological systems. The Intergovernmental Panel on Climate Change Fourth and Fifth Assessment Reports (AR4 and AR5) documented observed responses to climate change across a wide range of systems as well as regions.

Figure 18.3 of the Working Group II report of AR5 summarizes a range of impacts observed around the world based on a detailed analysis of the peer-reviewed literature. This set of web pages reproduces Figure 18.3 and Tables 18-5 to 18-9 from the Working Group II report and the references explicitly mentioned in the tables. The published tables in the AR5 can be found here.

[image - chart - Figure 18-3. Global patterns of observed climate change impacts reported since AR4. Each filled symbol in the top panels indicates a class of systems for which climate change has played a major role in observed changes in at least one system within that class across the respective region, with the range of confidence in attribution for those region-wide impacts indicated by the bars. Regional-scale impacts where climate change has played a minor role are shown by outlined symbols in a box in the respective region. Sub-regional impacts are indicated with symbols on the map, placed in the approximate area of their occurrence. The impacted area can vary from specific locations to broad areas such as a major river basin. Impacts on physical (blue), biological (green), and human (red) systems are differentiated by color. This map represents a graphical synthesis of Tables 18-5, 18-6, 18-7, 18-8, and 18-9. Absence of climate change impacts from this figure does not imply that such impacts have not occurred. It means, instead, that it has not yet been (or perhaps never will be) detected and/or attributed. (High resolution graphic is available here)

Tables

References

Guidance documents and support materials

Citation

Cramer, W., G.W. Yohe, M. Auffhammer, C. Huggel, U. Molau, M.A.F. da Silva Dias, A. Solow, D.A. Stone, and L. Tibig, 2014: Detection and attribution of observed impacts. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979-1037.

Data Citation

IPCC. 2017. Intergovernmental Panel on Climate Change (IPCC) Observed Climate Change Impacts Database Version 2.01. Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at http://sedac.ipcc-data.org/ddc/observed_ar5/. (date of download).

Acknowledgements

IPCC AR5 Chapter 18:
Coordinating Lead Authors:
Wolfgang Cramer (Germany/France), Gary W. Yohe (USA)
Lead Authors:
Maximilian Auffhammer (USA), Christian Huggel (Switzerland), Ulf Molau (Sweden), Maria Assunção Faus da Silva Dias (Brazil), Andrew Solow (USA), Dáithí­ A. Stone (Canada/South Africa/USA), Lourdes Tibig (Philippines)
Contributing Authors:
Laurens Bouwer (Netherlands), Mark Carey (USA), Graham Cogley (Canada), Dim Coumou (Germany), Yuka Otsuki Estrada (USA/Japan), Eberhard Faust (Germany), Gerrit Hansen (Germany), Ove Hoegh-Guldberg (Australia), Joanna House (UK), Solomon Hsiang (USA), Lesley Hughes (Australia), Sari Kovats (UK), Paul Leadley (France), David Lobell (USA), Camille Parmesan (USA), Elvira Poloczanska (Australia), Hans Otto Pörtner (Germany), Andy Reisinger (New Zealand)
Review Editors:
Rik Leemans (Netherlands), Bernard Seguin (France), Neville Smith (Australia)
Volunteer Chapter Scientist:
Gerrit Hansen (Germany)

We also thank Robert S. Chen, Xiaoshi Xing, and Alyssa Fico (The Center for International Earth Science Information Network - CIESIN, Columbia University, USA), Rachel Warren (University of East Anglia, UK), Stewart Cohen (Environment and Climate Change, Canada), Gregory Insarov (Institute of Global Climate and Ecology, Roshydromet and Russian Academy of Sciences), Timothy R. Carter (Finnish Environment Institute - SYKE), Bruce Hewitson (University of Cape Town, South Africa)

Content last modified: 14 May 2018

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IPCC AR4 Observed Climate Change Impacts

Recent warming around the world has caused changes in many physical and biological systems. The Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008) documented observed responses to climate change across a wide range of systems as well as regions [1, 2]. In the database, responses in physical systems include shrinking glaciers in every continent, melting permafrost, shifts in spring peak river discharge associated with earlier snowmelt, lake and river warming (with effects on thermal stratification, chemistry, and freshwater organisms), and increases in coastal erosion. For terrestrial biological systems, changes documented in the database include shifts in spring events (e.g., earlier leaf unfolding, blooming date, migration, and timing of reproduction), species distributions, and community structure. Database observations also demonstrate changes in marine-ecosystem functioning and productivity, including shifts from cold-adapted to warm-adapted communities, phenological changes and alterations in species interactions. In each category, many of the data series are over 35 years in length.

For the database, observations were selected that (1) demonstrate a statistically significant trend in change in either direction in systems related to temperature or other climate change variable as described by the authors; and (2) contain data for at least 20 years between 1970 and 2004 (although study periods may extend earlier or later). For each observation, the data series is described in terms of system, region, longitude and latitude, dates and duration, statistical significance, type of impact, and whether or not land use was identified as a driving factor. System changes are taken from ~80 studies (of which ~75 are new since the IPCC Third Assessment Report) containing >29,500 data series. Observations in the database are characterized as a ?change consistent with warming? or a ?change not consistent with warming,? based on information from the underlying studies.

The locations of the observed physical and biological changes were overlaid on observed temperatures from 1970-2004 using two different gridded observed temperature data sets: HadCRUT3 [3] and GHCN-ERSST [4]. A spatial analysis showed that the agreement between the patterns of observed significant changes in natural systems and temperature change is very unlikely to be due to natural variability. Thus, the Intergovernmental Panel on Climate Change Fourth Assessment Report and Rosenzweig et al. (2008) concluded that it is likely that anthropogenic warming has had a discernible influence on many physical and biological systems at a global scale.

[image - chart - Figure SPM.1. Locations of significant changes in data series of physical systems (snow, ice and frozen ground; hydrology; and coastal processes) and biological systems (terrestrial, marine, and freshwater biological systems), are shown together with surface air temperature changes over the period 1970-2004. A subset of about 29,000 data series was selected from about 80,000 data series from 577 studies. These met the following criteria: (1) ending in 1990 or later; (2) spanning a period of at least 20 years; and (3) showing a significant change in either direction, as assessed in individual studies. These data series are from about 75 studies (of which about 70 are new since the Third Assessment) and contain about 29,000 data series, of which about 28,000 are from European studies. White areas do not contain sufficient observational climate data to estimate a temperature trend. The 2 x 2 boxes show the total number of data series with significant changes (top row) and the percentage of those consistent with warming (bottom row) for (i) continental regions: North America (NAM), Latin America (LA), Europe (EUR), Africa (AFR), Asia (AS), Australia and New Zealand (ANZ), and Polar Regions (PR) and (ii) global-scale: Terrestrial (TER), Marine and Freshwater (MFW), and Global (GLO). The numbers of studies from the seven regional boxes (NAM, …, PR) do not add up to the global (GLO) totals because numbers from regions except Polar do not include the numbers related to Marine and Freshwater (MFW) systems. Locations of large-area marine changes are not shown on the map. [Working Group II Fourth Assessment F1.8, F1.9; Working Group I Fourth Assessment F3.9b].]

The related IPCC Fourth Assessment Report (AR4) WGII Summary for Policymakers (923 KB PDF) and the Technical Summary (10.3 MB PDF) are available online.

Disclaimer: Questions regarding this database should be addressed to Cynthia Rosenzweig. Part of the European data [5] was contributed by Annette Menzel. Users should contact her regarding these data. Their contact emails are included in the data description of the database.

The data can be viewed (and downloaded) following the links below.

Data Tables

Data tables in HTML-format
Data tables in Excel-format

References:

1. Rosenzweig, C., G. Casassa, D.J. Karoly, A. Imeson, C. Liu, A. Menzel, S. Rawlins, T.L. Root, B. Seguin, and P. Tryjanowski. 2007. Assessment of Observed Changes and Responses in Natural and Managed Systems. In M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden, and C.E. Hanson (eds.), Climate Change 2007: Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.

• Download PDF (Document is 2.72 MB)

2. Rosenzweig, C., D. Karoly, M. Vicarelli, P. Neofotis, Q.G. Wu, G. Casassa, A. Menzel, T.L. Root, N. Estrella, B. Seguin, P. Tryjanowski, C.Z. Liu, S. Rawlins, and A. Imeson. 2008. Attributing Physical and Biological Impacts to Anthropogenic Climate Change. Nature, 453(7193): 353-357.

• Download PDF (Document is 5.8 MB)
• There is also supplementary material (700 kB PDF)

3. Brohan, P., J.J. Kennedy, I. Harris, S.F.B. Tett, and P.D. Jones. 2006. Uncertainty Estimates in Regional and Global Observed Temperature Changes: A new data set from 1850. Journal of Geophysical Research-Atmospheres, 111, D12106.

4. Smith, T.M. and R.W. Reynolds. 2005. A Global Merged Land and Sea Surface Temperature Reconstruction Based on Historical Observations (1880?1997). Journal of Climate, 18(12): 2021-2036.

5. Menzel, A., T.H. Sparks, N. Estrella, E. Koch, A. Aasa, R. Ahas, K. Alm-K�bler, P. Bissolli, O. Braslavsk�, A. Briede, F.M. Chmielewski, Z. Crepinsek, Y. Curnel, �. Dahl, C. Defila, A. Donnelly, Y. Yolanda Filella, K. Katarzyna Jatczak, F. Finn M�ge, A. Antonio Mestre, �. Nordli, J. Pe�uelas, P. Pirinen, V. Remi?ov�, H. Scheifinger, M. Striz, A. Susnik, A. Vliet J.H. van, F.-E. Wielgolaski, S. Zach, and A. Zust. 2006. European Phenological Response to Climate Change Matches the Warming Pattern. Global Change Biology, 12(10): 1969-1976.

The original database was constructed at GISS with contributions from IPCC AR4 WGII Chapter 1 Authors. This version 1.0 of the database was compiled by a GISS-CIESIN team and was reviewed and approved for publication at the IPCC Data Distribution Centre (DDC) by the IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis (TGICA) 15th meeting at Geneva, Switzerland, on 19-21 November, 2008.

Data Citation

Rosenzweig, C., P. Neofotis, M. Vicarelli, and X. Xing (eds.). 2008. Intergovernmental Panel on Climate Change (IPCC) Observed Climate Change Impacts Database Version 1.0. Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at http://sedac.ciesin.columbia.edu/ddc/observed/. (date of download).

Acknowledgements

IPCC AR4 Chapter 1 Authors who contributed to the development of the database include: Gino Casassa (Centro de Estudios Cient�ficos, Chili), David J. Karoly (University of Melbourne, Australia), Anton Imeson (3D-Environmental Change, Netherlands), Chunzhen Liu (China Water Information Center, China), Annette Menzel (Technical University of Munich, Germany), Samuel Rawlins (Caribbean Epidemiology Center, Trinadad and Tobago), Terry L. Root (Stanford University, USA), Bernard Seguin (INRA Unit� Agroclim, France), Piotr Tryjanowski (Adam Mickiewicz University, Poland), Nicole Estrella (Technical University of Munich, Germany), and Qigang Wu (University of Oklahoma, USA).

We also thank Robert S. Chen, Marc Levy, Alex de Sherbinin and Maria Muniz (The Center for International Earth Science Information Network - CIESIN, Columbia University, USA), Richard H. Moss and Jose Marengo (Co-Chairs of IPCC TGICA), Timothy Carter (Finnish Environment Institute - SYKE, Finland), Gao Xuejie (Climate Modeling Center, China Meteorological Administration), Martin Manning (Climate Change Research Institute, Victoria University of Wellington, New Zealand), Tom Kram (Netherlands Environmental Assessment Agency, The Netherlands), Martin Juckes (IPCC Data Distribution Centre at British Atmospheric Data Centre, UK), and Michael Lautenschlager (IPCC Data Distribution Centre at World Data Centre for Climate, Germany).

Content last modified: 14 May 2018

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Environmental Data and Scenarios

Welcome to the Environmental Data Section of the Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC). Acknowledging that changes in environmental conditions other than climate may need to be considered when conducting climate change impact and vulnerability assessments, the Environmental Data pages of the DDC provide access tobaseline and scenario data for a range of non-climate conditions in the atmospheric, aquatic and terrestrial environments. These include data on atmospheric composition (e.g. carbon dioxide, ozone), land use and land cover, sea level, and water availability and quality. Most projections are consistent with the driving factors and emissions presented in the Special Report on Emissions Scenarios (SRES).

Explanations and illustrations of procedures for incorporating this information in impact and vulnerability assessments can be found on the DDC Guidelines pages. Data for other environmental variables will be added in due course.

Scenario data for the atmospheric environment

Carbon Dioxide

A number of gases and other atmospheric constituents may have important effects on the exposure unit. Perhaps the most important of these is carbon dioxide. CO2 is well mixed in the atmosphere, so observations of concentrations from a single site are adequate for most impact applications.

CO2 concentration is commonly required as a direct input to models of plant growth, since it can affect both the growth and water use of many plants. Since it is also a major greenhouse gas associated with global climate change, the CO2 concentration adopted should be consistent with concentrations during the climatological baseline period.

Conventionally, the baseline CO2 concentration is assumed fixed at a given level. This might be the reference concentration in which plants have been grown in CO2-enrichment experiments. Alternatively, it might be the default value assumed in an impact model, usually a value representative of the late 20th century. However, a word of caution is necessary when testing impact models for conditions over a 30-year or longer baseline period. CO2 concentrations have increased rapidly during the 20th century, and if the exposure unit is responsive to CO2, this temporal trend should be accounted for.

Further information and data relating to CO2 is available here.

Tropospheric ozone:

Another gas of importance in some impact studies is tropospheric ozone. This is toxic for a wide range of living organisms, its concentrations being highly variable in space and time, registering its highest concentrations over industrial regions under certain weather conditions. Time series of ozone concentrations are available for some regions, especially in developed countries. They are usually expressed in terms of background concentrations and peak concentrations. Global model estimates of ozone abundance and gridded model results are available from the DDC.

Stratospheric ozone:

Concentrations of stratospheric ozone have been measured operationally at many high latitude sites in recent years, especially following the discovery of the seasonal "ozone hole" over Antarctica in 1985. Ozone depletion is associated with increased ultraviolet radiation, which can be harmful for life on earth. Daily forecasts of exposure risk to UV-radiation are issued in many countries at mid to high latitudes, especially during the spring and early summer when levels of stratospheric ozone are generally at a minimum.

Sulphur and nitrogen compounds:

Concentrations of sulphur and nitrogen compounds, which are both major contributors to acid precipitation in many parts of the world, are also measured in some regions. Furthermore, it has been estimated that sulphate aerosol concentrations in industrial regions have contributed a cooling effect on climate in some regions in past decades, which has counteracted the warming effect of greenhouse gases.

Smoke and particulates:

Smoke and other particulate matter in the atmosphere, bi-products of fossil fuel burning, land clearance or other human activities, can have important regional impacts on visibility and human health. These are increasingly being observed using satellites as well as ground based instruments.

Sea level:

One of the key factors to evaluate for many impact studies in low lying coastal regions is the current level of the sea relative to the land. Globally, eustatic sea level (the volume of water in the oceans) appears to have been rising during the past century. However, there are large regional deviations in relative sea level from this global trend due to local land movements. Subsidence, due to tectonic movements, sedimentation, or human extraction of groundwater or oil, enhances relative sea-level rise. Uplift, due to post glacial isostatic rebound or tectonic processes, reduces or reverses sealevel rise. The main source of information on relative sea level is tide gauge records, and the major global data source is the Permanent Service for Mean Sea Level.

As a reference, most studies of vulnerability to sea-level rise use the mean sea-level at a single date. For instance, studies employing the IPCC Common Methodology use the level in 1990. However, to assess coastal vulnerability to sea-level effects, baseline tide gauge and wave height observations are required. These reflect tidal variations in combination with the effects of weather such as severe storms and atmospheric pressure variations.

Inland water levels:

The levels of lakes, rivers and groundwater also vary with time, usually for reasons related to the natural balance between water inflow (due to precipitation and runoff) and losses (due to evaporation and seepage). Human intervention can also affect water levels, through flow regulation and impoundment, land use changes, water abstraction and effluent return and large scale river diversions. Sometimes these fluctuations in levels can be very large (often much larger than mean changes anticipated in the future). Thus, where time series are available, it is important to be able to identify the likely causes of fluctuations (i.e. natural or anthropogenic), as this information could influence the selection of an appropriate baseline period.

Scenario data the terrestrial environment (land use and land cover - SRES)

Land cover and land use:

On land, data on land cover and land use change are of great importance in many impact studies. Geographical data and time series have been compiled by a number of research groups working at national, continental and global scale, based on satellite imagery, aerial photographs and ground survey. Many datasets have been collected as part of a major international research effort - the Land Use and Land Cover Change Programme (LUCC) of the International Geosphere Biosphere Programme (IGBP) and International Human Dimensions Programme on Global Environmental Change (IHDP). For instance, a global integrated model, IMAGE 2, has been used to study the dynamics of land use change. The model was initialised using baseline land use data from 1970. A continually updated time series of observed global land use up to the 1990s can then be used to test the model's predictions during the period after 1970. National land cover/land use statistics have also been tabulated by the IPCC and are available from the DDC.

Soil:

Baseline information is also commonly required on the state of the soil where this has been changing over time, for example, nutrient status, pH and salinity. Data sources for this information tend to be national or regional in scope.

Agricultural practices:

In agriculture, data on farm management practices are of vital importance in describing the reference conditions. This covers, for instance, fertilizer applications, use of pesticides and herbicides, tillage practices, stocking rates and irrigation. Baseline information on these is important, not only because they have been responsible for dramatic increases in productivity in many regions in recent decades, but also because they have contributed to soil erosion or pollution of soils, surface waters and groundwater in many regions. Data for different countries are collected annually by the United Nations Food and Agriculture Organization.

Biodiversity:

There has been considerable concern in recent years about the endangerment and loss of natural species, mainly attributable to human activities. There have been a number of national and international initiatives to document and catalogue biodiversity, andbaseline statistics representative of the 1990s have been compiled for each country by the World Conservation Monitoring Centre, were published for an IPCC report on Regional Impacts of Climate Change (available from this page).

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Content last modified: 18 June 2013

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Data from computer simulations and projections

Computational models play an increasingly central role in underpinning our understanding of the environment, society. The DDC contains data produced from Integrated Assessment Models (IAMs), Carbon-cycle Models, General Circulation Models, and Earth System Models.

• Emissions scenarios (from Integrated Assessment Models):
  • RCP Emissions and Land Use scenarios used in AR5
  • SRES Emissions scenarios used in TAR and AR4
  • IS92 Emissions scenarios user in SAR
• Carbon Dioxide: Projected emissions and concentrations
• Global climate model output
  • Results from GCM-Runs for the Fifth Assessment Report (AR5)
  • Results from GCM-Runs for the Fourth Assessment Report (AR4) based on the IPCC-SRES scenarios
  • Results from GCM-Runs for the Third Assessment Report (TAR) based on the IPCC-SRES scenarios
  • Results from GCM-Runs based on the IPCC-IS92a (or similar) emission scenarios
  • Results from GCM-Runs user in the First Assessment Report (FAR) (subset)
• Global climate model output: period-averages
  • Model output described in the 2007 IPCC Fourth Assessment Report (SRES scenarios)
  • Model output described in the 2001 IPCC Third Assessment Report (SRES scenarios)
  • Model output described in the 1995 IPCC Second Assessment Report (IS92 scenarios)
• Global climate model output: global-means (AR4)

IPCC IS92 Scenarios

Six alternative IPCC scenarios (IS92a to f) were published in the 1992 Supplementary Report to the IPCC Assessment. These scenarios embodied a wide array of assumptions affecting how future greenhouse gas emissions might evolve in the absence of climate policies beyond those already adopted. The different worlds that the scenarios imply, in terms of economic, social and environmental conditions, vary widely and the resulting range of possible greenhouse gas futures spans almost an order of magnitude. The assumptions for the IS92 scenarios came mostly from the published forecasts of major international organisations or from published expert analyses. Most of these were subject to extensive review. The premises for the IS92a and IS92b scenarios most closely resemble and update those underpinning the original SA90 scenario used in the First Assessment Report of the IPCC in 1990. IS92a has been widely adopted as a standard scenario for use in impact assessments, although the original IPCC recommendation was that all six IS92 emissions scenarios be used to represent the range of uncertainty in emissions. Population rises to 11.3 billion by 2100 and economic growth averages 2.3 % per annum between 1990 and 2100, with a mix of conventional and renewable energy sources being used. The highest greenhouse gas emissions result from the IS92e scenario that combines, among other assumptions, moderate population growth, high economic growth, high fossil fuel availability and eventual phase out of nuclear power. At the other extreme, IS92c has a CO2 emissions path that eventually falls below its 1990 starting level. It assumes that population first grows, then declines by the middle of next century, that economic growth is low, and that there are severe constraints on fossil fuel supply.

These scenario data can be viewed (and downloaded) following the links below.

Data Tables

Data tables in HTML-format
Data tables in Excel-format

Reference:

• J. Leggett, W.J. Pepper, R.J. Swart, J. Edmonds, L.G. Meira Filho, I. Mintzer, M.X. Wang, and J. Watson. 1992. "Emissions Scenarios for the IPCC: an Update", Climate Change 1992: The Supplementary Report to The IPCC Scientific Assessment, Cambridge University Press, UK, pp. 68-95

• W. J. Pepper, R.J. Leggett, R.J. Swart, J. Wasson, J. Edmonds and I. Mintzer. 1992. "Emission Scenarios for the IPCC An Update, Assumptions, Methodology, and Results", US Environmental Protection Agency, Washington, D.C.

Content last modified: 14 May 2018

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SRES emissions scenarios

The IPCC published a new set of scenarios in 2000 for use in the Third Assessment Report (Special Report on Emissions Scenarios - SRES). The SRES scenarios were constructed to explore future developments in the global environment with special reference to the production of greenhouse gases and aerosol precursor emissions. They use the following terminology:

• Storyline: a narrative description of a scenario (or a family of scenarios), highlighting the main scenario characteristics and dynamics, and the relationships between key driving forces.
• Scenario: projections of a potential future, based on a clear logic and a quantified storyline.
• Scenario family: one or more scenarios that have the same demographic, politico-societal, economic and technological storyline.

The SRES team defined four narrative storylines (see Figure 1), labelled A1, A2, B1 and B2, describing the relationships between the forces driving greenhouse gas and aerosol emissions and their evolution during the 21st century for large world regions and globally . Each storyline represents different demographic, social, economic, technological, and environmental developments that diverge in increasingly irreversible ways.

[image - graph - Figure 1: Schematic illustration of the four SRES storylines]

In simple terms, the four storylines combine two sets of divergent tendencies: one set varying between strong economic values and strong environmental values, the other set between increasing globalization and increasing regionalization . The storylines are summarized as follows (Nakicenovic et al., 2000):

• A1 storyline and scenario family: a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and rapid introduction of new and more efficient technologies.
• A2 storyline and scenario family: a very heterogeneous world with continuously increasing global population and regionally oriented economic growth that is more fragmented and slower than in other storylines.
• B1 storyline and scenario family: a convergent world with the same global population as in the A1 storyline but with rapid changes in economic structures toward a service and information economy, with reductions in material intensity, and the introduction of clean and resource-efficient technologies.
• B2 storyline and scenario family: a world in which the emphasis is on local solutions to economic, social, and environmental sustainability, with continuously increasing population (lower than A2) and intermediate economic development.

After determining the basic features of each of the four storylines, including quantitative projections of major driving variables such as population and economic development taken from reputable international sources (e.g. United Nations, World Bank and IIASA), the storylines were then fully quantified using integrated assessment models, resulting in families of scenarios for each storyline. In all 40 scenarios were developed by six modelling teams. All are equally valid, with no assigned probabilities of occurrence. Six groups of scenarios were drawn from the four families: one group each in the A2, B1 and B2 families, and three groups in the A1 family, characterising alternative developments of energy technologies: A1FI (fossil intensive), A1T( predominantly non-fossil) and A1B (balanced across energy sources). Illustrative scenarios1 were selected by the IPCC to represent each of the six scenario groups.

The DDC provides quantitative listings of the SRES scenarios, as well as an interpretation - using the same simple models as were used with the IS92 scenarios above - of what these different scenarios signify for future global temperature and sea-level change. The assumptions underlying these emissions scenarios (i.e. population, economic growth, etc.) are also described.

The final and complete SRES scenario data can be viewed and downloaded following the link below. (Version 1.1 of the GHG emissions associated with the 40 SRES scenarios provided here are not completely identical to those in Appendix VII of the SRES report as published by Cambridge University Press (2000). In a small number of cases slight corrections were made after the publication of the document to prevent negative emissions which occurred as a result of the standardization procedure. The changes, which were necessary in just a few scenarios and future years, were relatively small. The corrected numbers presented here are recommended for use in further impacts, adaptation and mitigation analysis. The final gridded emissions data for sulfur dioxide, methane, carbon monoxide, nitrous oxides, and non-methane VOCs in the six illustrative scenarios were based on the version 1.1. emissions.)

SRES Final Data Tables

SRES Final Data Tables in HTML-format and Excel-format

Reference:

Nakicenovic, N. et al (2000). Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, U.K., 599 pp. Available online at: http://www.grida.no/climate/ipcc/emission/index.htm

Content last modified: 14 May 2018

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Global Climate Model Experiment Data Archive

Welcome to the Global Climate Model Data Archive section of the Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC). This page is the main entry point for users who want to retrieve either data (FAR to AR4 monthly mean; AR5 in different frequencies) available at DDC or information on the models used.

About DDC GCM data archive

The DDC uses the CERA database which is run by the World Data Center Climate (WDCC) at DKRZ. Detailed information on the CERA database is available on the Web. You can look here to get more information.

The data is stored on a tape archive which is associated with the (local) database CERA. A data request will initiate a retrieval mechanism that will take some time to transfer the data from tape to disk, therefore users may have to wait before the requested data is transferred.

Data is provided in NetCDF for AR5 and otherwise in GRIB format (machine independent, self-descriptive binary formats). If you need data in GZIP (compressed ASCII) format you'll have to convert the binary data locally.

Information on both formats and the internal data structure is given here.

You can select between:

• Results from GCM-Runs for the Fifth Assessment Report (AR5) based on the IPCC-RCP scenarios*
• Results from GCM-Runs for the Fourth Assessment Report (AR4) based on the IPCC-SRES scenarios*
• Results from GCM-Runs for the Third Assessment Report (TAR) based on the IPCC-SRES scenarios
• Results from GCM-Runs based on the IPCC-IS92a (or similar; SAR) emission scenarios
• Results from GCM-Runs user in the First Assessment Report (FAR) (subset)

* You can get a subset of these IPCC-DDC data on storage medias here.

Download Statistics

Annual statistics and reports are available starting for 2014 at Annual IPCC-DDC statistics. Monthly statistics of the number of downloads and the download volume for IPCC-DDC data are available online:

• Total monthly downloads of IPCC-DDC data as number of downloads and download volume,
• Monthly downloads of IPCC-DDC AR5 data as number of downloads and download volume,
• Monthly downloads of IPCC-DDC AR4 data as number of downloads and download volume.

GCM data validation

One of the criteria commonly used in selecting a GCM to be used in constructing regional climate scenarios for impact assessment is the performance of the GCM in simulating the present-day climate in the region. This is evaluated by comparing the model outputs with observed climate in the target region, and also over larger scales, to determine the ability of the model to simulate large scale circulation patterns. Examples of graphical comparisons between GCM outputs and observed climate for the 1961-1990 period for subcontinental world regions can be found here.

AR5 Scenarios

AR5 Scenarios are based on scenarios of the CMIP5 (Climate Model Intercomparison Project Phase 5). Details on CMIP5 Scenarios can be found in:
Taylor, K.E., R.J. Stouffer, G.A. Meehl (2012): An Overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485-498, doi:10.1175/BAMS-D-11-00094.1.
And details on the RCP Emissions and Land Use scenarios used in AR5 are described here.

Content last modified: 09 April 2018

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AR5 GCM data

Climate model results provide the basis for important components of IPCC assessments, including the understanding of climate change and the projections of future climate change and related impacts. The IPCC's Fifth Assessment Report (AR5) relies heavily on the Coupled Model Intercomparison Project, Phase 5 (CMIP5), a collaborative climate modelling process coordinated by the World Climate Research Programme (WCRP).

The CMIP5 archive has evolved through and beyond the IPCC 5th Assessment process, with modelling groups eager to contribute their best available data to the research community. The IPCC DDC provides access to two snapshots of the CMIP5 archive in the World Data Center Climate (WDCC):

• (i) the IPCC Working Group I snapshot, and
• (ii) the DDC Reference snapshot.

The IPCC WGI snapshot was collected at ETH Zurich to support the IPCC WGI AR5<

Great, thank you for sharing this information happy wheels

Truthout - 11/17/18 - (fair use)

"California Wildfires: Where Is the Climate Change Outrage?"

[photo - JOSH EDELSON / AFP / GETTY IMAGES - In this aerial photo, a burned neighborhood is seen in Paradise, California on November 15, 2018.]

By Bruce Melton, Truthout:

Unprecedented droughts, fires and floods are not the “new normal”: Climate change gets nonlinearly worse from here on out. Like an avalanche, the physics of warming determines that a little more warming doesn’t create a little more extremeness, but a lot more. Until we reduce greenhouse gas warming, it gets a lot worse every year. Reducing emissions alone does not reverse warming until after 2300. To reduce warming, we must eliminate all greenhouse gas emissions by 2050, plus remove near 1,000 gigatons of already emitted greenhouse gases from the atmosphere, according to the new 1.5°C report from the Intergovernmental Panel on Climate change.

Meanwhile, a third unprecedented wildfire in California has happened in the last 12 months. At the time of writing, nearly 12,000 structuresand 71 lives have been lost in Paradise, California, to the “Camp Fire” that began November 8. Before that, the July 2018 Mendocino Complex Fire burned 459,000 acres and set a record for the state’s largest fire. But the largest fire record before that was set by Thomas Fire, which burned 281,000 acres in December 2017. More than 10,000 structures have been lost in 2018 so far in the state, and more than 9,300 structures were lost in 2017.

The record-setting increases of these unheard-of extreme weather events continues to horrify the public. What will it take to allow us to treat climate change like it is the most important issue our society has ever faced, as top scientists say it is?

[image - graph - This list of California’s worst fires was compiled from Cal Fires top 20 lists for the largest, most destructive and deadliest wildfires back to 1914.]

Research from the University of California, Irvine tells us that, “The occurrence of extremely large Santa Ana (wind-caused) fires has increased abruptly since 2003.” Since then, burned area in the most extreme fires has increased by 70 percent and structures lost have increased by more than 200 percent, according to Cal Fire statistics (see list above). California’s population however, increased by only 12 percent since 2003. Why has this out-of-balance increase in fire behavior occurred?

A Longer Warm Season

Many of these recent astounding fires in California are human-related, but why has damage and burned area increased so much lately, when firefighting efforts have also proportionally increased?

Human-caused ignition is a compelling argument, but if so, impacts would be only slightly elevated from pre-2003 times because of the 12 percent population increase. Area burned since 2003 has nearly doubled and structures burned has nearly tripled, however. The reason for all of these catastrophes is plain and simple: It’s warmer now.

Warming has immense implications relative to increasing extremes. Basic physics tells us that warming increases evaporation nonlinearly. That is, a little warming creates a lot more drying of fuels. A longer warm season allows drying to compound, amplifying already-increased extreme fire behavior.

Anthony Westerling at the University of California, Merced, published US forest wildfire statistics across the country from 1970 through 2012 in the Philosophical Transactions of the Royal Society B in May 2016. Here’s what he had to say:

• US wildfire season has increased by more than 60 percent since the 1970s, from 138 days to 222 days, because of earlier onset of spring.
• The average burn time has increased nearly 800 percent, from six days to 52 days, because of deeper drying.
• Burned area increased 1,271 percent.
• Human-caused ignition has played a very small role in increasing wildfire trends.

The fingerprint of warming is unmistakable. It’s not increasing population. It’s not increased human-caused ignition sources. Is there anything else that it could be?

President Trump wrongly blames California’s forest management for the increase in extreme fires. Brian K. Rice, president of the California Professional Firefighters, has responded, saying that, “[N]early 60 percent of California forests are under federal management and another one-third under private control. It is the federal government that has chosen to divert resources away from forest management, not California.” Forest management doesn’t even apply to almost all of Southern California. For example, the Woolsey Fire in Thousand Oaks is in scrub typical of southern California, not timberlands.

The prevailing academic thought on forest management is that more dead fuels litter our forests from the Smokey-Bear-era of fire suppression efforts, and this allows more extreme fires. This is good science and certainly played a role in increasing burn behavior before climate warming rose above natural variation 10 or 15 years ago when these unprecedented extremes began to become so numerous.

Today, however, warming has consequences. One of them is that warming creates a drier world; and in a drier world, wildfire is more extreme. Another of these consequences is increased duration of Santa Ana wind events on a warmer planet. The Santa Ana wind that created the Thomas Fire was the longest Santa Ana wind event on record. Santa Ana winds are hot, dry, down-sloping winds from the continental interior that blow offshore in southern and central California, can reach speeds in excess of 70 mph and generally occur in the fall.

If all of the above is not enough to say that climate disruption caused these catastrophes, why do catastrophes happen?

Catastrophes happen when natural event extremeness exceeds a threshold, and extreme fire behavior is just one of these thresholds. Flooding, wind damage, drought mortality, beach erosion, beetle kill, permafrost melt, ice sheet collapse, Gulf Stream shutdown — all of these things are not a problem until a threshold is crossed. We have crossed that threshold with wildfire and all of the above-mentioned impacts because our leaders have delayed action on regulating greenhouse gas emissions.

Without passing through a catastrophic threshold, catastrophes don’t happen. It’s time to stop believing these events are only enhanced or plausibly caused by climate change.

A little bit of warming makes a lot of difference. Nicholas J. Nauslar’s research on Southern California fires tells us that we’ve seen the most acute fire weather in more than two decades, the longest duration Santa Ana wind event in the 70-year record, the most extreme drought on record, the lowest fuel moisture on record and the driest March through December since 1895.

Pushing Back Against the Fairness Bias

The good news is that emissions can be managed; there’s plenty of science that says so. But we have been delayed for 30 years by economic, social, psychological and political forces that have prevented our leaders from acting.

There are players, or organizers of the climate change denial and delay movement, who are directly responsible, as is so well described as the “climate change counter-movement” by professor of sociology and environmental science Robert J. Brulle from Drexel University in 2014, and then detailed by Ruth McKie in 2018 at De Montfort University in 2018. These players have egregiously given license for citizens and leaders to dispute, deny and delay. It’s not just the political conservatives, but they brought it to the table.

The perceived debate, so roundly and unwittingly promoted by the media’s false balance, has influenced our society from stem to stern. This “false balance” is where journalistic norms of presenting both sides of the story equally and fairly allow inaccurate information to be presented with the same weight as accurate information. When all but just a few individual climate scientists agree but a little more than halfof Americans believe climate change is human-caused, there’s some powerful deceit going on.

All of us are influenced by false balance, from laypersons to advocates to leaders. Even climate scientists are impacted by the false balance because climate scientists are specialists. They focus on their own research agenda, whether it be ocean biochemistry, ice sheet geomorphology, dendrochronology, paleotempestology or scores of other disciplines.

Climate scientists don’t know it all, and many scientists don’t even know climate science. Scientists get their non-specialist information from the media, in general, just like the rest of us.

Logic will give us the correct answer, but we must be able to trust logic when the media is telling us differently. Logic will allow us to overcome the “fairness” bias propagated by the climate change counter-movement, so that the exact science can be understood by non-experts, and we can begin to address climate change before it becomes nonlinearly worse.

Bruce Melton

Bruce Melton is a professional engineer, environmental researcher, filmmaker, author and CEO of the Climate Change Now Initiative in Austin, Texas. The Climate Change Now Initiative is a nonprofit outreach organization reporting the latest discoveries in climate science in plain English. Information on his book, Climate Discovery Chronicles, can be found along with more climate change writing, climate science outreach and critical environmental issue documentary films at ClimateDiscovery.org. Images copyright Bruce Melton and the Climate Change Now Initiative except where referenced otherwise.

MORE BY THIS AUTHOR…

https://truthout.org/articles/california-wildfires-where-is-the-climate-change-outrage/

The Intercept - 11/7/18 - (fair use)

"THE FOSSIL FUEL INDUSTRY SPENT $100 MILLION TO KILL GREEN BALLOT MEASURES IN THREE STATES — AND WON"

By Kate Aronoff:

[Photo by Helen H. Richardson/The Denver Post/Getty Images -- An oil derrick pumps oil near a subdivision roundabout looking west on June 7, 2017 in Dacono, Colo.]

PATRICIA NELSON WAS eager for a fresh start when she moved her family from Louisiana back home to Weld County, Colorado, in 2016. Soon after, Nelson’s friend encouraged her to come out to a meeting where Lisa McKenzie, an environmental chemist and epidemiologist at the Colorado School of Public Health, was presenting her research on the health impacts of oil and natural gas drilling.

Weld County has one of the highest concentrations of oil and gas wells in the country — 23,000 within county limits. Its air quality carries an “F” rating from the American Lung Association, with infant mortality rates twice as high as those in surrounding counties. With around 50,000 active wells overall, Colorado just surpassed California to become America’s third-largest oil and gas producer after Texas and North Dakota.

“It was a crash course in fracking,” Nelson told me by phone. Colorado law, she learned, states that drilling operations have to be 1,000 feet away from school buildings, but that ordinance — known as a setback — doesn’t include surrounding school properties, like playgrounds or soccer fields. There, as McKenzie would explain, kids playing and running around breathe harder and heavier, increasing the amount of poisoned air that enters their lungs and bloodstream.

All of this hit too close to home: As she also learned, oil companies had just been approved to open 24 new drill sites near her then-4-year-old son Diego’s school, the kindergarten through third grade campus of Bella Romero Academy; the drilling would take place just behind the fourth through eighth grade campus, where her niece and nephew were students. The decision to drill near Bella Romero at all — where 87 percent of attendees are students of color, and 90 percent fall below the poverty line — was made after parents at an overwhelmingly white school refused to have the same rigs in their kids’ backyards.

Shocked by what she discovered, Nelson joined a coalition that would later become known as Colorado Rising and traveled around the state, telling people about the stakes at her son’s school. Colorado Rising’s work included a push for Proposition 112, a ballot measure to mandate a 2,500-foot setback zone between drill sites and homes, schools, and other vulnerable areas. That measure was defeated 57 to 43 on Tuesday night, in large part thanks to a full-fledged freakout by the fossil fuel industry, which, with $40 million, outspent Prop 112 proponents by at least 40 to 1.

“This is much farther than we’ve gotten before, and we’re no longer going to accept this industry bullying us,” Nelson told me last night, celebrating the fact that Prop 112 even made it on the ballot. “We had a pretty good shot, but they definitely had way more resources than we did. I guess the oil and gas industry is just another example of money buying elections.”

Environmental initiatives on the ballot elsewhere in the country, vehemently opposed by industry groups, also flopped. A ballot initiative in Washington state to levy a $15 per ton carbon fee on polluters and invest the revenue in job creation, green infrastructure, and more was defeated 56 to 44 thanks to over $30 million from the oil and gas industry. In Arizona, electric utilities spent $31 million against Prop 127, which would have upped the state’s renewable portfolio standard, requiring the power sector to generate at least half its power from renewables by 2030. The proposition was largely bankrolled by liberal donor Tom Steyer via NextGen America, which poured $24 million in support, but it failed resoundingly, garnering just 30 percent of the vote.

[Photo by Helen H. Richardson/The Denver Post/Getty Images -- Ann Lee Foster, center, and Suzanne Spiegel, right, proponents of Proposition 112, hold back the tears as they concede defeat during the watch party for Proposition 112 at Big Trouble Restaurant on Nov. 6, 2018 in Denver, Colo.]

TUESDAY WASN’T AN unambiguous win for the industry, though — even in Colorado. Amendment 74 — polling high before the election and far better than Prop 112 — would have allowed property owners to sue local governments and the state for any infringement on their profits, but fell short of the 55 percent of votes needed to be grafted into Colorado’s constitution. The campaign for Amendment 74 was small compared to the fight against Prop 112 but still sizable, with $11.2 million raised by backers — about $10 million from oil and gas — and $6.3 by opponents under the banner Save Our Neighborhoods.

Still, one clear takeaway from the midterms ballot initiatives is that fossil fuel money can buy elections. Apparently, $100 million can buy four of them. “They’re putting up big numbers,” said Edgar Franks, a Bellingham-based labor organizer who helped draft and campaign for I-1631 with the environmental justice group Front and Centered. “You can tell that where this is actually a threat to the way that they do business, because they know it’s going to work.”

While Washington Gov. Jay Inslee backed I-1631 — having failed to get his own carbon fee through the legislature this spring — his Colorado counterpart was on the opposite side of the fight in his state. Prop 112 failed amid opposition not just from the oil and gas industry, but also from now-outgoing Colorado governor and former oil industry geologist John Hickenlooper, a Democrat, along with governor-elect Jared Polis and his Republican opponent. The Colorado Democratic Party, however, supported it. Polis’s opposition was ironic, given that he himself had spearheaded the push for a 2,000-foot setback rule several years ago before eventually withdrawing it from the ballot.

Despite Polis’s opposition to Prop 112, Colorado Rising is cautiously optimistic about working with his administration, a position bolstered by the fact that Democrats managed to flip the state legislature last night. “Polis clearly understands that fracking is dangerous near communities,” said Micah Parkin, a board member of Colorado Rising and executive director of 350 Colorado. “He may not agree with us on 2,500 feet, but he clearly gets that it doesn’t belong near our children’s schools and homes and water sources.”

The bar for improving Hickenlooper’s record on extraction has been set pretty low. The outgoing governor had threatened to call a special lame duck session of the state legislature in the event of Prop 112’s passage.“ It’s incredibly undemocratic,” said Parkin in advance of the vote. “The very idea that he would think it’s OK to turn around and ignore the will of the people, when thousands of his own constituents have worked so hard.”

The statement wasn’t unprecedented for Hickenlooper. In 2013, he openly threatened to sue any city that banned fracking within its borders and in fact, did sue Longmont and Fort Collins after they implemented restrictions on fracking. The state’s suit also undermined the legal standing of three other bans and moratoria. “Topics like these,” Nelson told me, “are the ones that enable the true colors of our representatives to come out. It just shows that he’s never been on the side of the people, he’s been on the side of industry.”

Predictably, then, the fight over Proposition 112 got ugly. Oil and gas interests painted the measure as an attempt to ban fracking in the state, though it in fact only added on to policies that already exist, which require a 500-foot setback from homes and 1,000-foot setback from schools. In collecting signatures this summer to get Prop 112 on the ballot, canvassers were followed and surrounded by paid protesters. “It would be between two and four of us who would plan out where to collect signatures,” Nelson said. “We would get there and 15 or 20 minutes later, these kids would show up with their signs. Unless they were following us, how would they know where we were?” She suspects at least some of them were University of Colorado students and had awkward encounters with them around town after encountering them while petitioning.

A political consultant hired by Colorado Rising this summer to help collect signatures also walked out of the state with 15,000 signatures, which were only retrieved after the group filed a lawsuit to have them returned.

The industry also publicly — and knowingly — overstated the impact of the measure. As Denver’s 9News found from a leaked report that industry groups had seen a report from the consultancy RS Energy stating that their losses would be far less than what they projected out over airwaves. The industry-friendly state regulator, the Colorado Oil and Gas Conservation Commission, had instead claimed in July that some 80 percent of non-federal land in Colorado — the only land covered by Prop 112 — would be off-limits to drilling. Yet the RS Energy report shows that companies would maintain access to 61 percent of the state’s oil and gas reserves. That’s partially because although companies would be barred from drilling within setback zones, horizontal drilling techniques — where the well mouth is within permitted drilling areas — could still access minerals under off-limits areas from up to a mile away.

While it would have been the country’s most ambitious regulatory check on the fossil fuel industry, Parkin and Nelson both emphasized that Prop 112 was a kind of method of last resort. Those looking to place more regulations on the oil and gas industry had tried to get bills passed through the legislature and appealed to the Colorado Oil and Gas Conservation Commission — all to no avail. “People have tried so many different ways to protect themselves over the years,” Parkin said, “and this was the last recourse.”

[Photo: Ted S. Warren/AP -- A supporter of Initiative 1631 holds a sign referencing the Nisqually Indian Tribe, on Oct. 17, 2018, during a rally supporting I-1631 in Lacey, Wash.]

IT WAS A similar story in Washington, where the legislature failed earlier this year to get a somewhat similar carbon tax measure through the legislature, falling short by “one or two votes.” A 2016 ballot initiative had failed to — by a margin of 20 percent — after drawing fire from the same groups now pushing for I-1631 over its lack of both investment in green projects and attention to the demands of climate and environmental justice groups. The campaign this time around was comparatively massive, drawing in a huge coalition of labor groups, indigenous communities, climate nonprofits, and more.

The biggest limiting factor to commonsense climate policies like Prop 112 and I-1631 isn’t either old school climate denial or bad design, but the sheer force of the fossil fuel industry’s seemingly endless capacity to pour money into elections. While theoretically supportive of carbon pricing and some vague sense of climate action, fossil fuel interests this cycle showed their cards: If it poses a threat to their profits, it’s going down. Fightbacks like the one posed to Amendment 74 are possible. Yet with Democrats taking the House — with several climate hawks now in their ranks — any push on climate there will have to reckon with the colossal funding might of the industry, and the kind of opposition Prop 112 faced from members of their own party sponsored by them.

On the heels of the latest IPCC report — which makes the need to decarbonize every sector of the economy painfully clear — there isn’t really an alternative to going toe to toe with those companies, no matter how much of a David and Goliath fight it might be.

“This isn’t over for me, personally. We have had a warning,” Nelson said, referencing that report, “that we either end our dependence on fossil fuels or things are going to get extremely rough for mankind. For me, it shows that it’s just about greed and money for this industry.”

https://theintercept.com/2018/11/07/midterm-elections-green-ballot-measures-fossil-fuel/

Wired - Science section - 11/9/18 - (fair use)

THE TERRIFYING SCIENCE BEHIND CALIFORNIA’S MASSIVE CAMP FIRE

By Mat Simon:

[image by JOSH EDELSON/GETTY IMAGES -- It used to be that fires destroyed exurbs or scattered enclaves. Now they plow through cities.]

Editor’s note: This is a developing story about California’s Camp Fire, Hill Fire, and Woolsey Fire. We will update it as more information becomes available.

At 6:30 am on November 8, a wildfire of astounding proportions and speed broke out in Northern California. Dubbed the Camp Fire, at one point it was burning 80 acres a minute.

When it hit the town of Paradise, home to 27,000 people, those buildings became yet more fuel to power the blaze. It has destroyed almost 13,000 structures. For perspective, the previously most destructive wildfire in state history, Tubbs Fire that raged through the city of Santa Rosa last year, destroyed 5,500 total structures.

The death toll so far stands at 76. That makes it by far the deadliest wildfire in California history. Almost 1,300 people are still missing. And the blaze is 60 percent contained, with an estimated full containment date of Nov. 30.

“We're seeing urban conflagrations, and that's the real phase change in recent years,” says Stephen Pyne, a wildfire expert at Arizona State University. It used to be that fires destroyed exurbs or scattered enclaves. “But what's remarkable is the way they're plowing over cities, which we thought was something that had been banished a century ago.”

The Camp Fire horror show, which burned 70,000 acres in 24 hours, and has now reached 150,000 acres, is a confluence of factors. The first is wind—lots of it, blasting in from the east. “We have a weather event, in this case a downslope windstorm, where, as opposed to the normal westerly winds, we get easterly winds that are cascading off the crest of the Sierra Nevada,” says Neil Lareau, an atmospheric scientist at the University of Nevada, Reno.

A windstorm barreling from the east set the stage for this disaster. It’s a normal phenomenon that comes from the jet stream, which this time of year grows stronger. North and south “meanders” in the jet stream, known as troughs and ridges, get amplified. These cold air masses travel through the Great Basin in Nevada and spill over the Sierra Nevada Mountains in eastern California. Big meanders set up very-high-pressure areas that accelerate winds.

“Then they get local accelerations on top of that as they flow down the mountain ranges, kind of like water over a dam,” Lareau says. Some areas in California are particularly prone to downsloping winds. “Unfortunately, right where the Camp Fire is is one of those places.”

“I always like to say nothing good comes from an east wind in California,” Lareau adds.

As the air descends at an accelerating pace, it warms up and drives the relative humidity down. Which brings us to our second factor in the horror show: fuel—lots of it. It may be November, but California is still extremely dry, which means plenty of vegetation that’s primed to go up in flames.

The east winds further dehydrate the vegetation. This is where something called the evaporative demand drought index comes in. “You can think about it as how thirsty the atmosphere is,” Lareau says. “How strongly does the atmosphere want to pull water out of the vegetation and out of the ground?”

Very strongly, in the case of the Camp Fire and those downslope winds. So it isn’t just a matter of things being generally dry for the season in Northern California—ground and vegetation moisture fluctuates day to day, too. Scientists can calculate this in part by going out and cutting vegetation, weighing it, drying it out, and weighing it again.

“This tells us those fuels have been drying out really, really rapidly over the past few days and into this event,” Lareau says. Just take a look at the eerily prescient tweet below from meteorologist Rob Elvington the day before the Camp Fire broke out.

[image graph -- "Worse than no rain is negative rain. Evaporative Demand Index (EDDI) is maxing out for some areas for the last 4 weeks." #CAwx -- Rob Elvington​]

So you’ve got hot, dry gusts of 40 or 50 miles per hour from the northeast pushing the fire, and the fire is itself creating wind, further accelerating the conflagration. As it moves along, embers fly out of the front of the fire. “As the fuels get drier, a smaller and smaller spark can leapfrog the fire through the landscape,” Lareau says. “That's just another way this thing comes up and bites you.”

“It's hot, dry, and windy, are your ingredients,” he adds. “We checked off all three here.”

That’s probably why the city of Paradise has suffered such astonishing losses. Urban areas aren’t supposed to burn, at least they haven’tbeen supposed to since San Francisco in 1906. They’ve been designed and built with better materials (read: a whole city isn’t made of wood alone anymore) and more defensible spaces. But with a conflagration like the Camp Fire, it can overwhelm an urban area by setting off hundreds or thousands of tiny fires, perhaps miles ahead of the main fire itself. There’s no single line to put up a fight, so firefighters are overwhelmed.

“It looks like it's another case where you've got billions and billions of embers riding with the wind,” Pyne says. “It only takes one ember to take out a house or a hospital. If there's any point of vulnerability, all those embers will find it.”

Shortly after the Camp Fire broke out, the Hill Fire erupted in Southern California near Thousand Oaks. And yet another, the Woolsey Fire, has burned 100,000 acres and destroyed at least 1,130 structures.

It was no coincidence that these fires landed all at once. “Literally the same air mass is what's causing the beginnings of a strong Santa Ana event ongoing now, as this air mass sags south through California,” Lareau says.

North or south, the state is extremely dry already. But these warm winds ripping through the Sierras are only making matters worse, siphoning what little moisture California’s vegetation has left. While the winds will likely die down a bit over the next few days, they’re due to pick back up again Sunday, which could bring still more fires.

This is what a climate change reckoning looks like. “All of it is embedded in the background trend of things getting warmer,” Lareau says. “The atmosphere as it gets warmer is thirstier.” Like a giant atmospheric mosquito, climate change is sucking California dry.

The consequence is fires of unprecedented, almost unimaginable scale. California cities are no longer safe from fire, and with climate change, things are only bound to get worse from here. Consider that seven of the 20 most destructive fires in state history have burned just in the last year.

“Mass shootings and mass burnings,” Pyne says. “Welcome to the new America.”

https://www.wired.com/story/the-terrifying-science-behind-californias-massive-camp-fire/

A Listing of Participants

https://themarketswork.com/2018/05/01/a-listing-of-participants/

And.....

Clinton Crime Family’s 50 Organizations: TICK TOCK

https://coreysdigs.com/clinton-foundation/clinton-crime-family-fifty-organizations-tick-tock/

Sorry celery in my teeth.

Governor moonbeam is a great example as to why CA is where it currently is sitting financially.

The article on "white" women is perfect. I do not currently listen for more than the first segment and then only if there is not a wannabee leftie/socialist legislator on as a guest, but knowing Hartmann is a repeatable puppet it is probably safe to assume he is still ranting about "white" people, especially men in general. His banter is sooo predictable and pathetic. He loves to divide the population into segments that he feels will benefit the communist party world where he resides. He will never forgive his parents for being born "white". Barack Obama was termed a black man but George Zimmerman who listed himself as Hispanic was a "white" villain.

The world is upside down thanks to progressivism.

I get it and totally agree. Houston, Dallas, San Antonio, and Austin, plus all of the southern border are all blue. The vast bulk of it comes from the very rich offspring of parents that earned it in the oil and cattle business, and then those that migrated because of a business move to avoid taxes or just understanding TX is a great place to live.

That said, someday we will be all blue as the "Bernie freeshit" Sanders mentality sets in with the younger generation many of whom believe they are entitled to free everything. Wait until they gt their first paycheck. Occasional cortex who was just elected out of NYC is a perfect example. When asked how all the freeshit was going to be paid for, her response was, a little form the "rich" (I always loved that category) and the rest will come from the government. How's that for brilliance. The new darling of the left.

Blood Mary time.

Thanks for the info. I completely disregarded the tech industry. DUH.

I really had no idea pot was that big of a deal. TX is flooded with CA (among others) because industry is flowing out of CA due to high taxes. It was my assumption that those in OR and WA was due to the same. It won't be long and pot will be legal everywhere and a new stream of revenue for the States. Trump would do well to take the federal restrictions off just before the election in 2020 and he would snatch some votes away from those who feel they are entitled to win every time.

I AM surprised that the mayors of cities like Seattle and Portland don't appear to make the slightest attempt at cleaning up the mess their cities have become. I know it has affected their tourism and convention business. I guess once the leftie/socialists are in power, deterioration of the core city is guaranteed.

Chilling the vodka right now.

Morning HotCoffee, More from Portland a once beautiful city, now full of California cast outs and right wing loonies has become maybe one of the worst places to live in the country. The alt-leftie/socialists seem to have taken over the city and they think it is their duty to clash with anybody that has other beliefs. The far right goes there to taunt them. Must be fun living with two divergent groups battling each other while trying to stay out of the needles and poop in the sidewalk.

After watching the vote count in Ga and Fl, I am positive the democrat party is preparing for a lot of this type behavior as they feel every election win should be theirs. The loser in GA is making a fool of herself but the overall winner and new darling of the democrat party is clearly Brenda Snipes the vote altering idiot in Broward county FL.

Clearly she and mad Maxine Waters should;d team up for a 2020 run for President. It is very apparent she is not taking any money for her corruption, otherwise she would not look like she does.

later on,