Guidance to use the climate data (from U. Idaho)The MACA dataset is gridded on a 4-km scale. The region of interest may overlap more than one grid point in the dataset. Likely, there will be a need for some average value of a metric over the area of interest. There can be much spatial variability especially with complex terrain or near coastlines so one needs to be careful about averaging meteorological variables. We suggest that target metrics be derived at each grid point in the area of interest. Then after a look at the results over the spatial grid, a decision can be made about which grid points are of real concern to the project goals and averages can be estimated only for those grid points.
Data generators are aware that users of the MACA dataset will try many different approaches to come to conclusions about changes in future climate. Unfortunately, some of the possible ways of using the dataset are not obviously erroneous or non-ideal. Therefore, the University of Idaho team has come up with a short list of advice on using the MACA dataset. First, here are some advised general steps for the calculation of a change projected by a set of GCMs.
General Steps in Analysis
The following order for an analysis of a change in metric is advised:
- Define a metric
- Calculate the metric in the historical period (first average over all years, then average over all pixels in the study area)
- Calculate the metric for the future period (first average over a 30 year period, then average over all pixels in the study area)
- Calculate the change in the metric between future and historical periods.
- Calculate statistics of the average changes on the set of models (multi-model mean gives signal, multi-model standard deviation gives measure of uncertainty)
Climate is usually determined by taking an average of at least 30 years of data (WMO best practice), where more is better.
Creating a baseline
To establish a historical baseline for a certain area, the full 56 year historical period (1950-2005) from each GCM should be utilized. The MACA process maps GCM values to values from the training observation dataset, so that only the full 56 years will give the proper statistics held in the training dataset. Utilizing a subset of the 56 year historical period, will not guarantee that the extremes typical of the data will be seen or that the resulting statistics will be typical of the historical period.
To evaluate the changes predicted by each GCM, the same statistical quantity should be derived for both a future period (i.e. 2040-2070) and for the full historical period (1950-2005) for each GCM. The difference (or ratio) between these quantities for these periods will reflect the future anomaly predicted by the GCM.
The MACA group provides future predictions from several different GCMs and for different future scenarios. It is never good practice to obtain statistical information from a sample size of only 1 or 2. It is ideal to use as many of the models as possible in getting a good signal on the predicted change for the future (as well as some information on error from the uncertainty between the models).
The GCMs are not expected to get a correct value on any day/year of either the historical or future periods. Therefore, the meteorological values on any day/year should not be averaged over the GCMs before determining a metric. Instead, the metric should be calculated for each model independently for the season and time period and only metrics should be averaged over the GCMs.
Guidance to use the vegetation projections (from CBI)Vegetation Model Description
MC2, the C++ version of MC1, is a dynamic global vegetation model (DGVM) that simulates vegetation distribution, biogeochemical cycling and wildfire in a highly interactive manner. The model always simulates competition between woody and herbaceous lifeforms. It does not simulate individual species.
Each grid cell is simulated independently (time before space), with no cell-to-cell communication. However, drought conditions that trigger fires often occur region-wide, resulting in similar fire effects across contiguous cells. The model simulates potential vegetation that would occur without direct intervention by humans but indirect effects such as increasing greenhouse gas concentrations, grazing and fire suppression can be included.
The biogeography module simulates vegetation types, each composed of two lifeforms, herbaceous and woody, the relative dominance of which varies as a function of climatic conditions. Lifeforms include evergreen needleleaf, deciduous needleleaf, evergreen broadleaf and deciduous broadleaf woody lifeforms, as well as C3 (cool) and C4 (warm) herbaceous lifeforms. The balance between woody and herbaceous is determined by simulated competition between them for light, water and nutrients, as mediated by fire. There are 38 vegetation types available, 14 within the temperate zone (Table 1) defined by both biomass and smoothed climate indices.
The biogeochemistry model is a modified version of the CENTURY model (Metherell et al. 1993) that simulates the cycling of carbon and nitrogen among plant parts, multiple classes of litter, and soil organic matter pools. It also simulates actual and potential evapotranspiration (AET and PET) and soil water content in multiple soil layers, the number of which depends on the total soil depth that is input to the model. Woody lifeform leaf and grass moisture contents are calculated as functions of the ratio of available water to PET. These are interpreted as live fuel moisture contents by the fire module and affect fire behavior.
Woody and herbaceous lifeform production rates are based on maximum monthly rates that are interpolated from lifeform-dependent parameter values, depending on the lifeform set by the biogeography module. Maximum production rates are multiplied by limiting-factors scalars related to temperature, water and atmospheric CO2 that differ for woody vs. herbaceous lifeforms (Bachelet et al. 2001).
The fire module simulates the occurrence, behavior and effects of fire and was originally designed to project large, severe fires that account for the bulk of observed fire impacts in the conterminous US (Lenihan et al. 1998; 2008). It includes a set of mechanistic fire behavior and effects functions (Rothermel 1972; Peterson and Ryan 1986; van Wagner 1993) embedded in a structure that enables two-way interactions with the biogeochemistry and biogeography modules. Live and dead fuel loads in 1-hr, 10-hr, 100-hr and 1000-hr fuel classes are estimated from the carbon pools simulated by the biogeochemistry module. Allometric functions relate woody carbon pool sizes to height, crown base height and bark thickness for an average-sized tree. The empirical parameters used are required to determine when crown fires occur and to project fire effects on vegetation.
Daily moisture contents of the different fuel classes and potential fire behavior are calculated each day based on pseudo-daily data interpolated from the monthly climate inputs. Moisture contents of plant parts passed from the biogeochemistry module determine live fuel moisture contents. A combination of the Canadian Fine Fuel Moisture Code (Van Wagner and Pickett 1985) and the National Fire Danger Rating System (Bradshaw et al. 1983) is used to estimate dead fuel moisture contents.
Potential fire behavior (including rate of spread) is calculated each day based on daily-interpolated fuel loads, moisture contents and weather. Potential fire behavior is modulated by vegetation type, which affects fuel properties and realized wind speeds (higher for grasslands than forest). Actual fire is projected whenever the calculated rate of spread is greater than zero and user-specified thresholds are exceeded for the fine fuel moisture code (FFMC) and the build up index (BUI) of the Canadian fire weather index system. These two indices are inverse functions of fine fuel and coarser fuel moisture contents, respectively, as specified by Van Wagner and Pickett (1985). Only one fire is simulated per year per cell on the first day when all thresholds are exceeded.
Rogers et al. (2011) added an algorithm to MC1 to simulate intentional fire suppression by humans using thresholds for three fire intensity metrics: rate of spread (ros), fireline intensity (fli) and energy release component (erc). Fire suppression was applied from 1951 and only large "catastrophic fires" above the set thresholds could escape due to build up of fuel loads and deep drought conditions
The MC2 model requires inputs of soil depth, texture and bulk density. Soils data from Kern (1995; 2000) were obtained from Dr. Ray Drapek (USFS PNW) who reprojected the original 1km data to a 4km grid using the "majority" rule such that the soil-related value that occupies the majority of the area of a particular grid-cell gets assigned to the entirety of that grid-cell.
Climate inputs to the model include monthly precipitation, mean vapor pressure or dewpoint temperature, and mean daily maximum and minimum temperatures averaged over each month. Historical climate data (1895-2010) were acquired from the PRISM group at Oregon State University (Daly et al. 2008) at 2.5 arc-minute grid resolution and reprojected to the 4km grid.
Future climate projections (2010-2100) were originally acquired from the WCRP (World Climate Research Programme) CMIP5 (Coupled Model Intercomparison Project phase 5) multi-model database website (http://cmip-pcmdi.llnl.gov/cmip5/data_getting_started.html) and downscaled to 4km at the University of Idaho using the MACA method (Abatzoglou 2012).
The DGVM runs in three distinct phases producing results that are used as input for the next phase. First, the static biogeography model MAPSS (Neilson 1995) uses one-year of monthly mean climate (historical average for 30 years, 1895-1924) to generate a map of potential vegetation distribution at the beginning of the 20th century. MAPSS assumes that steady state occurs when all the soil available water is used up during the driest month of the year. During the second part of the equilibrium phase, the DGVM biogeochemistry module uses iteratively the same average climate (1895-1924) used by MAPSS to calculate the size of the carbon and nitrogen pools associated with each vegetation type on each pixel while allowing for prescribed vegetation-specific fire return intervals. The equilibrium phase ends when the resistant soil carbon pool size changes by less than 1% from one year to the next. Consequently the duration of this phase varies across the map depending on the type of vegetation cover (from a few decades in the Great Plains grasslands up to 3000 years in the rain forests of the PNW). During the 2nd or spinup phase, the model is run, also iteratively, using a detrended monthly historical climate time series (1895 to 2010) to capture the interannual variability and allow for readjustments of vegetation type and carbon pool sizes in response to dynamic wildfires. The time series is adjusted such that the climate variable means match the first 30 years of the historical period and allow for a smooth transition between spinup and transient historical climate. The spinup phase ends when the net biological production (net ecosystem production minus carbon consumed by wildfire) reaches an equilibrium state near zero (600 years for this project). During the third transient phase, the model is run first with a time series of historical climate data and then with future climate projections that include both interannual variability and long term trends.
How does one choose relevant climate projections?Mote et al. (2011) suggests the following guidelines for using climate model outputs for impact and climate diagnostics research:
- Understand to which aspects of climate your problem or decision is most sensitive (e.g., which climate variables - e.g. heat or rain, which statistical measures of these variables - e.g. seasonal average or extremes, and at what space and time scales).
- Determine which climate projection information is most appropriate for the problem or decision (e.g., variables - e.g. precipitation or snowpack, scales in space and time).
- Understand the limitations of the method you select (scale - e.g. complex terrain, level of details about landsurface characteristics - homogeneous landcover in climate model grid cells).
- Obtain climate projections based on as many simulations, representing as many models and emissions scenarios, as possible. We recommend using at least 10 models.
- It may be worth the effort to evaluate the relevant variables against observations (e.g. does it simulate PDO cycles, monsoon), just to be cognizant of model biases, but recognize that most studies have found little or no difference in culling or weighting model outputs.
- Understand that regional climate projection uncertainty stems from uncertainties about (1) the drivers of change (e.g., greenhouse gases, aerosols), (2) the response of the climate system to those drivers, and (3) the future trajectory of natural variability.
- Use the ensemble to characterize consensus not only about the projected mean but also about the range and other aspects of variability.
How many climate models should one choose?
- While evaluating climate model credibility is useful, studies have shown that climate projections from a random set of climate models yields results similar to those from the best models. Therefore it may not be absolutely necessary to cull or weight models before integrating them into decision-making process.
- One should also be cognizant that using all available model outputs (ensembles of opportunity) will not encompass the full range of potential futures.
- However, incorporating only a limited sample of projections is not suggested as it provides a limited sample.
- Instead, we advocate for at least 10 models to be considered in analyses.
How reliable are the climate projections?Model evaluation has been conducted at Oregon State University to assess how well CMIP5 models simulate historical climate of the Pacific Northwest (Rupp et al., 2013). This evaluation asks how well models simulate (i) monthly temperature and precipitation over the 20th century, (ii) spatial patterns over the northeast Pacific and western North America, (iii) teleconnection of the El Nino-Southern Oscillation to temperature and precipitation over the Pacific Northwest, (iv) interannual to decadal variability of precipitation and temperature over the Pacific Northwest, and (v) seasonal temperature trends. This analysis reveals a set of model rankings illustrated below and in further detail in (Rupp et al., 2013).
Figure 1: CMIP5 models ranked according to 18 performance metrics (Rupp et al. 2013). Ranking is based on the first 6 principal components (filled blue circles). The open symbols show the models error scores using the first 4, 5, and all principal components (PCs). The best scoring model has a normalized error score of 0.
Why do MACA-downscaled historical climate data not match station data?The MACA dataset is composed of 4-km grid cells. The incongruence between gridded data and station data can be rather pronounced in complex terrain, necessitating that the downscaled projections be adjusted for the station locations. This process should be done by first identifying the grid-cell which is co-located with each station and then performing a secondary bias correction procedure of the grid-cell data to that station data. For variables such as radiation and winds where station data are not available, interpolation of the 4-km value at the center of the cell to the station location is acceptable. If you need assistance with tailoring the downscaled data to stations, please inquire with John Abatzoglou (firstname.lastname@example.org) for your options.
What climate models were used?
|GCM (general circulation model) or ESM (Earth System Model)||Origin||Atmosphere resolution|
|Gridcell size degree Lat x Lon|
|L: # vertical levels|
|BCC-CSM1-1||Beijing Climate Center, China Meteorological Administration||2.8x2.8|
|BCC-CSM1-1-M||Beijing Climate Center, China Meteorological Administration||1.12x1.12|
|BNU-ESM||College of global change and earth system science, Beijing Normal University, China||2.8x1.4|
|CanESM2||Canadian Center for Climate Modelling and Analysis (Canada)||2.8x2.8|
|CESM1-BGC||Community earth system model contributors||1.25x.94|
|CESM1-CAM5||Community earth system model contributors||1.25x.94|
|CESM1-FASTCHEM||Community earth system model contributors||1.25x.94|
|CESM1-WACCM||Community earth system model contributors||2.5x.1.89|
|CMCC-CESM||Centro Euro-Mediterraneo per I Cambiamenti Climatici||3.75x3.71|
|CMCC-CM||Centro Euro-Mediterraneo per I Cambiamenti Climatici||.75x.75|
|CNRM-CM5||Meteo France and CNRS (France)||1.4x1.4|
|CSIRO-MK3-6.0||Commonwealth Scientific and Industrial Research Organization, Queensland Climate Change Center of Excellence (Australia)||1.8x1.8|
|FGOALS-s2||National Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid Dynamics and Institute of Atmospheric Physics, Chinese Academy of Sciences (China)||2.8x21.6|
|FIO-ESM||First Institute of Oceanography||2.81x2.79|
|GISS-E2-H||NASA-Goddard Institute for Space Studies (USA)||2.5x2.0|
|HadCM3||Meteorological Office Hadley Center, UK||3.75x2.5|
|HadGEM2-AO||Meteorological Office Hadley Center, UK||1.88x1.25|
|HadGEM2-CC||Meteorological Office Hadley Center, UK||1.88x1.25|
|HadGEM2-ES||Meteorological Office Hadley Center, UK||1.88x1.25|
|INM-CM4||Institute for Numerical Mathematics (Russia)||2.0x1.5|
|IPSL-CM5A-LR||Institut Pierre Simon Laplace (France)||3.75x1.8|
|IPSL-CM5A-MR||Institut Pierre Simon Laplace (France)||2.5x1.25|
|MIROC5||Atmosphere and Ocean Research Institute (U. Tokyo), National Institute for Environmental Studies, Japan Agency for Marine-Earth Science and Technology (Japan)||1.4x1.4|
|MIROC-ESM||Atmosphere and Ocean Research Institute (U. Tokyo), National Institute for Environmental Studies, Japan Agency for Marine-Earth Science and Technology (Japan)||2.8x2.8|
|MIROC-ESM-CHEM||Atmosphere and Ocean Research Institute (U. Tokyo), National Institute for Environmental Studies, Japan Agency for Marine-Earth Science and Technology (Japan)||2.8x2.8|
|MPI-ESM-LR||Max Planck Institute for Meteorology (Germany)||1.88x1.87|
|MPI-ESM-MR||Max Planck Institute for Meteorology (Germany)||1.88x1.87|
|MRI-CGCM3||Meteorological Research Institute (Japan)||1.1x1.1|
|NorESM1-M||Norwegian Climate Center||2.5x1.9|
|NorESM1-ME||Norwegian Climate Center||2.5x1.9|
What emission scenarios were used?
|Atmospheric CO2 concentration in 2100 in ppm||Temperature change||Global Mean Sea Level Rise (m) at 2090-99 relative to 1980-99 (Source IPCC 2007 and Jevrejeva et al. 2012)|
|2090-99 relative to 1980-99|
|(median value in parenthesis)|
|SRES A1Fi||958||2.4-6.4 (4.0)||0.26-0.59|
|RCP 8.5||936||3.8-5.7 (4.6)||0.81-1.65|
|SRES A2||846||2.0-5.4 (3.4)||0.23-0.51|
|SRES A1B||703||1.7-4.4 (2.8)||0.21-0.48|
|RCP 6.0||670||2.5-3.6 (2.9)||0.6-1.26|
|SRES B2||611||1.4-3.8 (2.4)||0.2-0.43|
|SRES A1T||575||1.4-3.8 (2.4)||0.20-0.45|
|SRES B1||544||1.1-2.9 (1.8)||0.18-0.38|
|RCP 4.5||538||2.0-2.9 (2.4)||.52-1.10|
|RCP 2.6||421||1.3-1.9 (1.5)||.36-.83|
GlossaryAnalogues as in "MACA" (see below).
AOGCM: see Climate Model.
Anomaly: Difference (or ratio) between a projection and the historical baseline.
Baseline (excerpt from IPCC 2013): The baseline (or reference) is the state against which change is measured. A baseline period is the period relative to which anomalies are calculated.
Carbon dioxide (CO2)(excerpt from IPCC 2013): A naturally occurring gas, also a by-product of burning fossil fuels from fossil carbon deposits, such as oil, gas and coal, of burning biomass, of land use changes and of industrial processes (e.g., cement production). It is the principal anthropogenic greenhouse gas that affects the earth's radiative balance. It is the reference gas against which other greenhouse gases are measured and therefore has a Global Warming Potential of 1.
Carbon dioxide (CO2) fertilization (excerpt from IPCC 2013): The enhancement of the growth of plants as a result of increased atmospheric carbon dioxide (CO2) concentration.
Climate (excerpt from IPCC 2013): Climate in a narrow sense is usually defined as the average weather, or more rigorously, as the statistical description in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years. The classical period for averaging these variables is 30 years, as defined by the World Meteorological Organization. The relevant quantities are most often surface variables such as temperature, precipitation and wind. Climate in a wider sense is the state, including a statistical description, of the climate system.
Climate change (excerpt from IPCC 2013): refers to a change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or external forcings such as modulations of the solar cycles, volcanic eruptions and persistent anthropogenic changes in the composition of the atmosphere or in land use. Note that the Framework Convention on Climate Change (UNFCCC), in its Article 1, defines climate change as: 'a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods'. The UNFCCC thus makes a distinction between climate change attributable to human activities altering the atmospheric composition, and climate variability attributable to natural causes.
Climate model (excerpt from IPCC 2013): A numerical representation of the climate system based on the physical, chemical and biological properties of its components, their interactions and feedback processes, and accounting for some of its known properties. Coupled Atmosphere-Ocean General Circulation Models (AOGCMs) provide a representation of the climate system that is near or at the most comprehensive end of the spectrum currently available. There is an evolution towards more complex models with interactive chemistry and biology. See ESMs.
Climate Normals: NOAA's computation of Climate Normals is in accordance with the recommendation of the World Meteorological Organization (WMO), of which the United States is a member. While the WMO mandates each member nation to compute 30-year averages of meteorological quantities at least every 30 years (1931 - 1960, 1961 - 1990, 1991 - 2020, etc.), the WMO recommends a decadal update, in part to incorporate newer weather stations. Meteorologists and climatologists regularly use Normals for placing recent climate conditions into a historical context. In addition to weather and climate comparisons, Normals are utilized in seemingly countless applications such as regulation of power companies, energy load forecasting, crop selection and planting times, construction planning, building design, and many others.
Climate prediction (excerpt from IPCC 2013): A climate prediction or climate forecast is the result of an attempt to produce (starting from a particular state of the climate system) an estimate of the actual evolution of the climate in the future, for example, at seasonal, interannual or decadal time scales. Because the future evolution of the climate system may be highly sensitive to initial conditions, such predictions are usually probabilistic in nature.
Climate projection (excerpt from IPCC 2013): A climate projection is the simulated response of the climate system to a scenario of future emission or concentration of greenhouse gases and aerosols, generally derived using climate models. Climate projections are distinguished from climate predictions by their dependence on the emission/concentration/radiative forcing scenario used, which is in turn based on assumptions concerning, for example, future socioeconomic and technological developments that may or may not be realized.
Climate scenario (excerpt from IPCC 2013): A plausible and often simplified representation of the future climate, based on an internally consistent set of climatological relationships that has been constructed for explicit use in investigating the potential consequences of anthropogenic climate change, often serving as input to impact models. Climate projections often serve as the raw material for constructing climate scenarios, but climate scenarios usually require additional information such as the observed current climate. A climate change scenario is the difference between a climate scenario and the current climate.
Climate system (excerpt from IPCC 2013): The climate system is the highly complex system consisting of five major components: the atmosphere, the hydrosphere, the cryosphere, the lithosphere and the biosphere, and the interactions between them. The climate system evolves in time under the influence of its own internal dynamics and because of external forcings such as vol¬canic eruptions, solar variations and anthropogenic forcings such as the changing composition of the atmosphere and land use change.
Climate velocity: measure of the velocity over the ground, in a particular location and in a potentially changing climate, which would be necessary to maintain a constant value for a particular measure of climate. For instance, in a warming climate, an organism might be able to maintain a constant temperature by moving uphill; the speed and direction, which would be needed to maintain a constant temperature would be the climate velocity with respect to temperature.
DGVM: dynamic global vegetation model, simulates vegetation shifts as well as biogeochemical cycles. Some DGVMs include disturbance. They are often used as the basis for the land-surface submodel of earth system models.
Downscaling (excerpt from IPCC 2013): Downscaling is a method that derives local- to regional- scale (10 to 100 km) information from larger-scale models or data analyses. Two main methods exist: dynamical downscaling and empirical/statistical downscaling. The dynamical method uses the output of regional climate models, global models with variable spatial resolution or high-resolution global models. The empirical/statistical methods develop statistical relationships that link the large-scale atmospheric variables with local/regional climate variables. In all cases, the quality of the driving model remains an important limitation on the quality of the downscaled information.
Drought (excerpt from IPCC 2013): A period of abnormally dry weather long enough to cause a serious hydrological imbalance. Drought is a relative term; therefore any discussion in terms of precipitation deficit must refer to the particular precipitation-related activity that is under discussion. For example, shortage of precipitation during the growing season impinges on crop production or ecosystem function in general (due to soil moisture drought, also termed agricultural drought), and during the runoff and percolation season primarily affects water supplies (hydrological drought). Storage changes in soil moisture and groundwater are also affected by increases in actual evapotranspiration in addition to reductions in precipitation. A period with an abnormal precipitation deficit is defined as a meteorological drought. A megadrought is a very lengthy and pervasive drought, lasting much longer than normal, usually a decade or more.
Earth System Model (ESM) (excerpt from IPCC 2013): A coupled atmosphere-ocean general circulation model in which a representation of the carbon cycle is included, allowing for interactive calculation of atmospheric CO2 or compatible emissions. Additional components (e.g., atmospheric chemistry, ice sheets, dynamic vegetation, nitrogen cycle, but also urban or crop models) may be included.
Emission scenario (excerpt from IPCC 2013): A plausible representation of the future development of emissions of substances that are potentially radiatively active (e.g., greenhouse gases, aerosols) based on a coherent and internally consistent set of assumptions about driving forces (such as demographic and socioeconomic development, technological change) and their key relationships. Concentration scenarios, derived from emission scenarios, are used as input to a climate model to compute climate projections. In IPCC (1992) a set of emission scenarios was presented which were used as a basis for the climate projections in IPCC (1996). These emission scenarios are referred to as the IS92 scenarios. In the IPCC Special Report on Emission Scenarios (Nakićenović et al. 2000) emission scenarios, the so-called SRES scenarios, were published, some of which were used, among others, as a basis for the climate projections presented in Chapters 9 to 11 of IPCC (2001) and Chapters 10 and 11 of IPCC (2007). New emission scenarios for climate change, the four Representative Concentration Pathways, were developed for, but independently of, the present IPCC assessment.
Equivalent carbon dioxide (CO2) concentration (excerpt from IPCC 2013): The concentration of carbon dioxide that would cause the same radiative forcing as a given mixture of carbon dioxide and other forcing components. Those values may consider only greenhouse gases, or a combination of greenhouse gases and aerosols. Equivalent carbon dioxide concentration is a metric for comparing radiative forcing of a mix of different greenhouse gases at a particular time but does not imply equivalence of the corresponding climate change responses nor future forcing. There is generally no connection between equivalent carbon dioxide emissions and resulting equivalent carbon dioxide concentrations.
Equivalent carbon dioxide (CO2) emission (excerpt from IPCC 2013): The amount of carbon dioxide emission that would cause the same integrated radiative forcing, over a given time horizon, as an emitted amount of a greenhouse gas or a mixture of greenhouse gases. The equivalent carbon dioxide emission is obtained by multiplying the emission of a greenhouse gas by its Global Warming Potential for the given time horizon. For a mix of greenhouse gases it is obtained by summing the equivalent carbon dioxide emissions of each gas. Equivalent carbon dioxide emission is a common scale for comparing emissions of different greenhouse gases but does not imply equivalence of the corresponding climate change responses.
Extreme weather event (excerpt from IPCC 2013): An extreme weather event is an event that is rare at a particular place and time of year. By definition, the characteristics of what is called extreme weather may vary from place to place in an absolute sense. When a pattern of extreme weather persists for some time, such as a season, it may be classed as an extreme climate event, especially if it yields an average or total that is itself extreme (e.g., drought or heavy rainfall over a season).
General circulation (excerpt from IPCC 2013): The large-scale motions of the atmosphere and the ocean as a consequence of differential heating on a rotating Earth. General circulation contributes to the energy balance of the system through transport of heat and momentum.
General Circulation Model (GCM) - see climate model
Greenhouse effect (excerpt from IPCC 2013): The infrared radiative effect of all infrared-absorbing constituents in the atmosphere. Greenhouse gases, clouds, and (to a small extent) aerosols absorb terrestrial radiation emitted by the earth’s surface and elsewhere in the atmosphere. These substances emit infrared radiation in all directions, but, everything else being equal, the net amount emitted to space is normally less than would have been emitted in the absence of these absorbers because of the decline of temperature with altitude in the troposphere and the consequent weakening of emission. An increase in the concentration of greenhouse gases increases the magnitude of this effect; the difference is sometimes called the enhanced greenhouse effect. The change in a greenhouse gas concentration because of anthropogenic emissions contributes to an instantaneous radiative forcing. Surface temperature and troposphere warm in response to this forcing, gradually restoring the radiative balance at the top of the atmosphere.
Greenhouse gas (GHG) (excerpt from IPCC 2013): Greenhouse gases are those gaseous constituents of the atmosphere, both natural and anthropogenic, that absorb and emit radiation at specific wavelengths within the spectrum of terrestrial radiation emitted by the earth's surface, the atmosphere itself, and by clouds. This property causes the greenhouse effect. Water vapor (H2O), carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4) and ozone (O3) are the primary greenhouse gases in the earth's atmosphere. Moreover, there are a number of entirely human-made greenhouse gases in the atmosphere, such as the halocarbons and other chlorine- and bromine- containing substances, dealt with under the Montreal Protocol. Beside CO2, N2O and CH4, the Kyoto Protocol deals with the greenhouse gases sulphur hexafluoride (SF6), hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs).
Land use and Land use change (excerpt from IPCC 2013): Land use refers to the total of arrangements, activities and inputs undertaken in a certain land cover type (a set of human actions). The term land use is also used in the sense of the social and economic purposes for which land is managed (e.g., grazing, timber extraction and conservation). Land use change refers to a change in the use or management of land by humans, which may lead to a change in land cover. Land cover and land use change may have an impact on the surface albedo, evapotranspiration, sources and sinks of greenhouse gases, or other properties of the climate system and may thus give rise to radiative forcing and/or other impacts on climate, locally or globally.
MACA: Multivariate Adaptive Constructed Analogues (Abatzoglou 2012). MACA is a statistical method for downscaling Global Climate Models (GCMs) from their native coarse resolution to a higher spatial resolution that captures both the scales relevant for impact modeling while preserving time-scales and patterns of meteorology as simulated by GCMs. This method has been shown to be slightly preferable to direct daily interpolated bias correction in regions of complex terrain due to its use of a historical library of observations and multivariate approach (Abatzoglou and Brown, 2011). Variables that are downscaled include 2-m maximum/minimum temperature, 2-m maximum/minimum relative humidity, 10-m zonal and meridional wind, downward shortwave radiation at the surface, 2-m specific humidity, and precipitation accumulation all at the daily timestep. http://maca.northwestknowledge.net/
Monsoon (excerpt from IPCC 2013): A monsoon is a tropical and subtropical seasonal reversal in both the surface winds and associated precipitation, caused by differential heating between a continental-scale land mass and the adjacent ocean. Monsoon rains occur mainly over land in summer.
Nonlinearity (excerpt from IPCC 2013): A process is called nonlinear when there is no simple proportional relation between cause and effect. Climate and biological systems contain many such nonlinear processes, resulting in a system with potentially very complex behavior. Such complexity may lead to tipping points.
Predictability (excerpt from IPCC 2013): The extent to which future states of a system may be predicted based on knowledge of current and past states of the system. Because knowledge of the climate system’s past and current states is generally imperfect, as are the models that utilize this knowledge to produce a climate prediction, and because the climate system is inherently nonlinear and chaotic, predictability of the climate system is inherently limited. Even with arbitrarily accurate models and observations, there may still be limits to the predictability of such a nonlinear system (AMS, 2000).
Process-based Model (excerpt from IPCC 2013): Theoretical concepts and computational methods that represent and simulate the behavior of real-world systems derived from a set of functional components and their interactions with each other and the system environment, through physical and mechanistic processes occurring over time.
Radiative forcing (excerpt from IPCC 2013): Radiative forcing is the change in the net, downward minus upward, radiative flux (expressed in W m-2) at the tropopause or top of atmosphere due to a change in an external driver of climate change, such as, for example, a change in the concentration of carbon dioxide or the output of the Sun. Sometimes internal drivers are still treated as forcings even though they result from the alteration in climate, for example aerosol or greenhouse gas changes in paleoclimates. The traditional radiative forcing is computed with all tropospheric properties held fixed at their unperturbed values, and after allowing for stratospheric temperatures, if perturbed, to readjust to radiative-dynamical equilibrium. Radiative forcing is called instantaneous if no change in stratospheric temperature is accounted for. The radiative forcing once rapid adjustments are accounted for is termed the effective radiative forcing. Radiative forcing is not to be confused with cloud radiative forcing, which describes an unrelated measure of the impact of clouds on the radiative flux at the top of the atmosphere.
Regional Climate Model (RCM) (excerpt from IPCC 2013): A climate model at higher resolution over a limited area. Such models are used in downscaling global climate results over specific regional domains.
Representative Concentration Pathways (RCPs) (excerpt from IPCC 2013): Scenarios that include time series of emissions and concentrations of the full suite of greenhouse gases and aerosols and chemically active gases, as well as land use/land cover (Moss et al., 2008). The word representative signifies that each RCP provides only one of many possible scenarios that would lead to the specific radiative forcing characteristics. The term pathway emphasizes that not only the long-term concentration levels are of interest, but also the trajectory taken over time to reach that outcome. (Moss et al., 2010). Four RCPs produced from Integrated Assessment Models were selected from the published literature and are used in the present IPCC Assessment as a basis for the climate predictions and projections: RCP2.6 - One pathway where radiative forcing peaks at approximately 3 W m-2 before 2100 and then declines. RCP4.5 and RCP6.0 - Two intermediate stabilization pathways in which radiative forcing is stabilized at approximately 4.5 W m-2 and 6.0 W m-2 after 2100. RCP8.5 - One high pathway for which radiative forcing reaches greater than 8.5 W m-2 by 2100 and continues to rise for some amount of time.
Runoff (excerpt from IPCC 2013): That part of precipitation that does not evaporate and is not transpired, but flows through the ground or over the ground surface and returns to bodies of water.
Scenario (excerpt from IPCC 2013): A plausible description of how the future may develop based on a coherent and internally consistent set of assumptions about key driving forces (e.g., rate of technological change, prices) and relationships. Note that scenarios are neither predictions nor forecasts, but are useful to provide a view of the implications of developments and actions.
Smoothing Kernel: discrete approximation of a smoothing function which is typically applied via a convolution operation to filter out high (spatial) frequency components of a multi-dimensional discrete signal. Since the derivative is definable only where the signal is smooth, signal smoothing is necessary before calculating the derivative.
Sobel Operator: matrix operation used for efficiently approximating the derivative (gradient) of a multi-dimensional discrete signal.
Snow water equivalent (SWE) (excerpt from IPCC 2013): The depth of liquid water that would result if a mass of snow melted completely.
Soil moisture (excerpt from IPCC 2013): Water stored in the soil in liquid or frozen form.
Soil temperature (excerpt from IPCC 2013): The temperature of the soil. This can be measured or modeled at multiple levels within the depth of the soil.
Spatial and temporal scales (excerpt from IPCC 2013): Spatial scales may range from local (less than 100 000 km2), through regional (100 000 to 10 million km2) to continental (10 to 100 million km2). Temporal scales may range from seasonal to geological (up to hundreds of millions of years).
SRES (Special Report on Emission Scenarios) scenarios (excerpt from IPCC 2013): emission scenarios developed by Nakićenović et al. (2000) and used, among others, as a basis for some of the climate projections shown in Chapters 9 to 11 of IPCC (2001) and Chapters 10 and 11 of IPCC (2007). The following terms are relevant for a better understanding of the structure and use of the set of SRES scenarios:
Scenario family: Scenarios that have a similar demographic, societal, economic and technical change storyline.
Four scenario families comprise the SRES scenario set: A1, A2, B1 and B2.
Storyline: A narrative description of a scenario (or family of scenarios), highlighting the main scenario characteristics, relationships between key driving forces and the dynamics of their evolution.
Streamflow (excerpt from IPCC 2013): Water flow within a river channel, for example expressed in m3 s-1. A synonym for river discharge.
Tipping point (excerpt from IPCC 2013): In climate, a hypothesized critical threshold when global or regional climate changes from one stable state to another stable state. The tipping point event may be irreversible.
Uncertainty (excerpt from IPCC 2013): A state of incomplete knowledge that can result from a lack of information or from disagreement about what is known or even knowable. It may have many types of sources, from imprecision in the data to ambiguously defined concepts or terminology, or uncertain projections of human behavior. Uncertainty can therefore be represented by quantitative measures (e.g., a probability density function) or by qualitative statements (e.g., reflecting the judgment of a team of experts).
Uncertainty in climate scenarios (Mote et al. 2011) comes from (i) the drivers of anthropogenic change associated with greenhouse gas, aerosols and land use change; (ii) the sensitivity of the climate system to these changes; and (iii) natural unforced variability. There are several ways to account for uncertainty in climate scenarios depending on the phenomenon of interest, time horizon and scale of interest. Consideration of which climate aspects are most relevant to a given decision-making process can lead to a more tailored and relevant discussion of these uncertainties.
- Uncertainty arising from anthropogenic forcing is less important than other sources of uncertainty before mid-century, but diverges substantially by late 21st century.
- Uncertainty arising from climate sensitivity and the manifestation of anthropogenic change at regional scales is model dependent and a significant source of uncertainty.
- Uncertainty arising from internal variability can be examined by looking at different ensemble members run with a common model and experiment (e.g., model runs differ only in that they have different initial conditions). This uncertainty can be particularly important for near-term projections and for variables with a low signal-to-noise ratio (e.g., precipitation).
BibliographyAbatzoglou J. T. 2012. Development of gridded surface meteorological data for ecological applications and modeling. International Journal of Climatology. doi: 10.1002/joc.3413AMS 2000
Hawkins, E., and R. Sutton. 2009: The Potential to Narrow Uncertainty in Regional Climate Predictions. Bull. Amer. Meteor. Soc. 90:1095-1107.
IPCC 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
Jevrejeva, S., J.C. Moore, and A. Grinsted. 2012. Saa level projections to AD2500 with a new generation of climate change scenarios. Global and Planetary Change 80-81:14-20.
Mote, P., L. Brekke, P.B. Duffy, and E. Maurer. 2011. Guidelines for constructing climate scenarios. EOS 92:257-264
Nakićenovic, N., J. Alcamo, G. Davis, B. de Vries, J. Fenhann, S. Gaffin, K. Gregory, A. Grübler, T. Y. Jung, T. Kram, E. Lebre La Rovere, L. Michaelis, S. Mori, T. Morita, W. Pepper, H. Pitcher, L. Price, K. Riahi, A. Roehrl, H-H Rogner, A. Sankovski, M. Schlesinger, P. Shukla, S. Smith, R. Swart, S. van Rooijen, N. Victor, and Z. Dadi. 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.
Rogelj, J., M. Meinhausen, and R. Knutti. 2012. Global warming under old and new scenarios using IPCC climate sensitivity range estimates. Nature Climate Change 2: 248-253.
Rupp, D. E., J. T. Abatzoglou, K. C. Hegewisch, and P. W. Mote. 2013. Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA, J. Geophys. Res. Atmos., 118, doi:10.1002/jgrd.50843.
ContactsDownscaled Climate Data Source: Dr. John Abatzoglou (email@example.com) and his postdoctoral fellow Katherine Hegewisch (firstname.lastname@example.org) For more details: http://maca.northwestknowledge.net/
Climate Data Source: - Historical: monthly data from 1895-2010 provided by the PRISM group led by Chris Daly, Oregon State University. For more details: http://www.prism.oregonstate.edu/ - Future: CMIP5, projections from 20 climate models (subset) and under future Representative Concentration Pathways (RCPs) RCP 4.5 and RCP8.5 (2006-2100). For more details: http://cmip-pcmdi.llnl.gov/cmip5/
Vegetation Projections Source: MC2, projections from 1895 to 2100 using 20 climate models projections under both the future Representative Concentration Pathway (RCPs) RCP 4.5 and RCP8.5. For more details: https://sites.google.com/site/mc1dgvmusers/ or http://bit.ly/1scWcfv
NOTE: CBI does not bear any liability for financial or other losses due the use of these projections.