1.5°C scenario

The 1.5°C scenario referred to in this study was developed as part of the “MESSAGE-GLOBIOM“(version 1.0) integrated assessment model (see Krey et al, 2016). It is the”MESSAGEix-GLOBIOM 1.0” scenario described by Grubler et al. 2018. The 1.5°C scenario was developed to limit warming to 1.5°C with a 50% probability, but may temporarily exceed that level by less than 0.1°C (see Rogelj et al. 2018). The global warming anomaly in 2091–2100 relative to the pre-industrial reference period (1850–1900) is 1.4°C.

Sources: Krey et al.(2016),Grubler et al.(2018),Rogelj et al. (2018)

2°C scenario

The 2.0°C scenario referred to in this study was derived using the Integrated Assessment Model IMAGE(version 3.0.1) by Stehfest et al. in 2014. The 2°C compatible scenario is assessed by IPCC SR1.5 as having at least a 66% probability of keeping warming below 2°C. The global warming anomaly in 2091-2100 relative to the pre-industrial reference period (1850-1900) is 1.7°C.

Sources: Stehfest et al.(2014)

Pre-Glasgow NDCs

The “Pre-Glasgow NDCs” scenario referred to in this study was developed by the MESSAGE-GLOBIOM(version 1.0) integrated assessment model by Krey et al. in 2016. It is the “MESSAGE-GLOBIOM 1.0”(version 1.0) scenario described by Grubler et al. in 2018. The global warming anomaly in the period 2091-2100 compared to the pre-industrial reference period (1850-1900) is 2.4°C.

Sources: Krey et al.(2016),Grubler et al.(2018),Rogelj et al. (2018)

Annual maximum

The highest value of an indicator or variable that is attained within one year


Caused by or resulting from human activity

Anthropogenic climate change

Climate change that can be identified as resulting from human activities. These activities include the burning of fossil fuels, deforestation, land use changes, livestock, fertilization, etc.



A body of rock and/or sediment that holds groundwater.

Attributable mortality

Mortality attributable to a specific exposure, which is usually expressed as the attributable number (ANAN ) or the attributable fraction (AFAF ) of deaths. In its simplest form, when presence/absence of an exposure is considered, it can be computed based on the relative risk (RR) as AF=(RR1)/RR=(P1P0)/P1AF = (RR - 1)/RR = (P_1 - P_0)/ P_1 and AN=nAFAN = n * AF where nn is the total number of observed deaths, P1P_1 is the probability of the outcome in the exposed group and P0P_0 is the probability of the outcome in the unexposed group.


A baseline period is a subset of the historical period that is used as a reference for evaluating changes in future periods.

Boundary conditions

Boundary conditions describe a set of input data needed to run a model, such as initial conditions and data about the surrounding world not simulated by the model. For example, for a lake model, this includes near-surface air temperature, wind speed, solar radiation and precipitation. For many of the studies presented on ISIPEDIA, the boundary conditions for impact models are provided by GCMs.


The CaMa-Flood (Catchment-based Macro-scale Floodplain) model is designed to simulate the hydrodynamics in continental-scale rivers.


Carbon Major

Highest Greenhouse gases (GHG) emissions emitting companies. Since 1988, more than half of global industrial GHGs can be traced to just 25 corporate and state fossil fuel producers

Clausius–Clapeyron relation

The Clausius–Clapeyron relation descibes how the maximum possible partial pressure of atmospheric water vapor changes with atmospheric temperature. From this relation one can derive that, under typical atmospheric conditions, the water-holding capacity of the atmosphere increases by about 7% for every 1°C rise in temperature.

Source: IPCC, Climate Change 2007: Working Group I: The Physical Science Basis, “FAQ 3.2 How is Precipitation Changing ?”

Climate forcing

Climate forcings are agents in our earth system that control our climate by adjusting the net radiation that Earth receives. They are sometimes called “climate driver”, “radiative forcing” or simply “forcing”. CO2 is a greenhouse gas that has a positive radiative forcing effect and therefore warms our climate. Some climate forcings are called “external” forcings because they occur from outside our atmosphere. There are natural external forcings, such as the output of gases and aerosols from volcanic eruptions or changes in the radiation that Earth receives due to solar variability. Anthropogenic sources of external forcings include introductions of greenhouse gases to our atmosphere by mining and combusting reservoirs of oil or natural gases.

Climate models

A representation of the the climate on a computer. These models range from relatively simple representing only a few variables to complicated systems where a large number of physical, chemical and biological processes are represented. By studying the results of models, we can better understand the current and future climate.



A period of abnormally dry weather long enough to cause a serious hydrological imbalance. There are different types of droughts. A hydrological droughts is when the water supply is low, such as in streams, reservoir and groundwater. An agricultural drought is caused by the limited availablity of soil moisture affecting crop yield. A meteorological drought is a drought when there is a prolonged period of little to no precipitation.


Environmental flow requirements

Environmental flow requirements are the minimum amount and quality of water required to sustain ecosystems and the human livelihoods that depend on it.

Acreman, Michael C., and Michael J. Dunbar. “Defining environmental river flow requirements? A review.” (2004).

Excess mortality

When associations of specific exposures (e.g., non-optimal temperatures) and outcomes (e.g., deaths) are considered, excess mortality is synonymous to attributable mortality. In other contexts, excess mortality simply describes the difference between the observed mortality in a population and a historical mortality baseline, computed by seasonally averaging observed mortality during previous years. Such excess mortality usually occurs due to seasonal influenza, pandemics (COVID-19), heat waves, or other large-scale public health threats.


The transfer of water from land to atmosphere by means of evaporation and transpiration by plants.


A unique pattern of climate response produced by each factor that affets climate.

Flood depth

Distance between the ground level and water surface.

Fluvial flooding

Overbank flooding occurs when the water level of a river or stream exceeds its channel.

General circulation models

General Circulation Models (GCM) is a computer representation of processes in the atmosphere, ocean, and land surface. It can be used to simulate changes in the global climate in respone to changes in greenhouse gasses.

General vegetation model

A General Vegatation Model (GVM) is a computer simulation of ecological processes (e.g., plant growth, phenology, wildfires). They can be used to model vegation dynamics in response to changes in the climate such as temperature and CO2 levels.


GFDL (Geophysical Fluid Dynamics Laboratory) has constructed NOAA’s first Earth System Models (ESMs). In one model, ESM2M, pressure-based vertical coordinates are used along the developmental path of GFDL’s Modular Ocean Model version 4.1. In the other, ESM2G, an independently developed isopycnal model using the Generalized Ocean Layer Dynamics (GOLD) code base was used.

Dunne, John P., et al. “GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: Physical formulation and baseline simulation characteristics.” Journal of climate 25.19 (2012): 6646-6665.

Dunne, John P., et al. “GFDL’s ESM2 global coupled climate–carbon earth system models. Part II: carbon system formulation and baseline simulation characteristics.” Journal of Climate 26.7 (2013): 2247-2267.

Global hydrological models

Global hydrological models (GHMs) model the land surface hydrologic dynamics of continental-scale river basins. Also see hydrological model.

Gosling, Simon N., and Nigel W. Arnell. “Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis.” Hydrological Processes 25.7 (2011): 1129-1145.

Grid cell

Smallest unit of the geographical domain covered by a numerical model, such as a climate or a hydrological model. Because of the complexity of the processes they represent and the limitations of computing power, these models divide up their modelling domain into a juxtaposition of “boxes” called grid cells. In the case of a climate model, the grid cells are tridimensional and are aligned along each spatial dimension. A climate model calculates the state of the climate system in each cell. Currently ISIMIP uses a grid size of 0.5 degrees or approximately 55 km at the equator.

Groundwater recharge

Vertical water flux from the soils to the groundwater (diffuse recharge) and from surface water bodies (point or focused recharge).

Small, Eric E. “Climatic controls on diffuse groundwater recharge in semiarid environments of the southwestern United States.” Water Resources Research 41.4 (2005).


Using a model to recreate past conditions.


H08 is one of the 13 global hydrology models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a global water sector outputs. H08 is a grid-cell based global hydrological model. It consists of six sub-models, namely land surface hydrology, river routing, reservoir operation, crop growth, environmental flow, and water abstraction. The formulations of sub-models are described in detail in Hanasaki et al. (2008a,b, 2010). In the standard simulation settings, H08 spatially covers the whole globe at a resolution of 0.5°×0.5° in order to assess geographical heterogeneity of hydrology and water use. Simulation period is typically for several decades and calculation interval is a day. The six sub-models exchange water fluxes and updates water storage at each calculation interval with the complete closure of water balance (the error is less than 0.01% of the total input precipitation). These characteristics enable the model to explicitly simulate the major interaction between natural water cycle and major human activities of the globe.

Also see global hydrological models.


Hanasaki, Naota, et al. “A global hydrological simulation to specify the sources of water used by humans.” Hydrology and Earth System Sciences 22.1 (2018): 789.


HadGEM2 stands for the Hadley Centre Global Environment Model version 2. The HadGEM2 family of models comprises a range of specific model configurations incorporating different levels of complexity but with a common physical framework. The HadGEM2 family includes a coupled atmosphere-ocean configuration, with or without a vertical extension in the atmosphere to include a well-resolved stratosphere, and an Earth-System configuration which includes dynamic vegetation, ocean biology and atmospheric chemistry.

Collins, W. J., et al. “Development and evaluation of an Earth-System model–HadGEM2.” Geosci. Model Dev. Discuss 4.2 (2011): 997-1062.

Historical period

In the context of climate science, the historical period defines the period of time that started with the start of industrial activities and their resulting impact on greenhouse gas concentrations, and extends until now. Climate conditions over the historical period are often compared with those during pre-industrial times or in the future.

Hydrological model

A computer simulation of hydrological features and processes, such as streamflow, groundwater recharge, evopotranspiration.

Ice phenology

An ice phenology refers to a dataset capturing the seasonality of thawing and freezing ice cover.

Internal climate variability

Internal climate variability describes variability generated within the climate system, which is best described by Hegerl et al. (2007):

Internal variability is present on all time scales. Atmospheric processes that generate internal variability are known to operate on time scales ranging from virtually instantaneous (e.g., condensation of water vapour in clouds) up to years (e.g., troposphere-stratosphere or inter-hemispheric exchange). Other components of the climate system, such as the ocean and the large ice sheets, tend to operate on longer time scales. These components produce internal variability of their own accord and also integrate variability from the rapidly varying atmosphere (Hasselmann, 1976). In addition, internal variability is produced by coupled interactions between components, such as is the case with the El-Niño Southern Oscillation (ENSO; see Chapters 3 and 8).


The IPSL-CM4 model developed at Institut Pierre Simon Laplace (IPSL) contributed to CMIP3. It is a classical climate model that couples an atmosphere-land surface model to a ocean-sea ice model. The IPSL-CM5 model, which is contributes to CMIP5, is an Earth System Model (ESM) that includes all the previous developments. It is a platform that allows for a consistent suite of models with various degrees of complexity, various numbers of components and processes, and different resolutions.

Dufresne, J-L., et al. “Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5.” Climate Dynamics 40.9-10 (2013): 2123-2165.


ISIMIP aims to improve global and regional risk management by advancing knowledge of the risks of climate change through integrating climateimpacts across sectors and scales in a multi-impact model framework. With this framework, ISIMIP offers guidance for consistently projecting the impacts of climate change across affected sectors and spatial scales. An international network of climate-impact modellers contribute to a comprehensive and consistent picture of the world under different climate-change scenarios.

For further information see


ISIMIP2a is the second ISIMIP simulation round, focusing on historical simulations (1971-2010) of climate impacts on agriculture, fisheries, permafrost, biomes, regional and global water and forests. This will serve as a basis for model evaluation and improvement, allowing for improved estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming.



Simulation round 2b from the Inter-sectoral Impact Model Intercomparison Project designed to provide robust information about the impacts of 1.5°C global warming and related low-emission pathways. The scientific rationale for the scenario design is described in detail in the paper by Frieler et al. 2017 ‘Assessing the impacts of 1.5°C global warming simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)’.


Frieler, Katja, et al. “Assessing the impacts of 1.5 C global warming–simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b).” Geoscientific Model Development (2017).


The ISIMIP3a protocol focuses on i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of the IPCC WGII-AR5 Chapter 18 definition. As a major update, it thus includes a counterfactual “no-climate change baseline” (detrended climate + observed socio-economic forcing).


Variable (e.g., temperature or rainfall) or combination of variables that is representative of the state of a system. For example, the amount of rainfall and evaporation are important factors in the drought indicator.


The IPCC is an intergovernmental body of the United Nations. Its role is to assess on a compre-hensive, objective, open and transparent basis the scientific, technical and socio-economic in-formation relevant to understanding the scientific basis of risk of human-induced climate change, its potential impacts and options for adaptation and mitigation.

From the IPCC website, “the IPCC is divided into three Working Groups […]. Working Group I deals with The Physical Science Basis of Climate Change, Working Group II with Climate Change Impacts, Adaptation and Vulnerability and Working Group III with Mitigation of Climate Change.”

The IPCC Working Group I (WGI) examines the physical science underpinning past, present, and future climate change. The WGI assessment combines observations, palaeoclimate, process studies, theory and modelling into a complete picture of the climate system and how it is changing, including the attribution (or causes) of change. As well as the global scale, WGI looks at variability and changes happening at a regional level, which is closely tied to how impacts and risks to human and natural systems are changing over time.


Lake stratification

In between mixing events, lakes develop a vertical temperature gradient between their bottom waters and the atmosphere. In the summer, this is caused by cool bottom waters contrasting with surface waters that warm from the sun. As a result, the top layers of the lake column are warmer and less dense than bottom layers. This process wherein lakes stratify in such thermal layers is described as stratification.

Null hypothesis

A null hypothesis is the baseline to hypothesis testing in science. The scientific method involves observing some pattern of change and formulating a hypothesis drescribing what might be responsible for that change. Tests and analyses are then performed to judge if this hypothesis was true. In this framework, the null hypothesis is often the antithesis of one’s actual hypothesis. To open the possibility that the hypothesized origin of an observed change is true, one must therefore disprove or “nullify” the null hypothesis. In the example of climate change, the we hypothesize that greenhouse gas emissions are responsible for climate warming. To open the possibility for this, we must disprove the null hypothesis that greenhouse gases are not responsible for warming and rather that this warming is driven by internal climate variability.


The European Center for Medium-Range Weather Forecasting (ECMWF) provides the newest (5th) generation of state of the art reanalysis datasets, including ERA5 and ERA5-Land. A reanalysis dataset provides continuous, gridded, high-resolution descriptions of our recent historical climate from 1950 onwards. In ERA5, this is done by assimilating observations into ECMWF’s Integrated Forecast System (IFS) model, which can be understood as using real observational data as anchor-points through which a physically-based global model can gap-fill the historical record of the atmosphere. ERA5-Land is an enhanced-resolution extraction of ERA5; it uses the land-model component of IFS and the lower atmosphere produced by ERA5 to describe the water and energy cycles over land with greater precision (Muñoz-Sabater et al. 2021).

Land cover

Physical material that the land is covered by. For example forest, water, buildings and asphalt.


Land use

The purpose for which the land is used. For example for agriculture, recreation, cities, etc.

Linear regression

A linear regression is a commonly used statistical technique to assess the relationship between different variables.


The model LPJmL (“Lund-Potsdam-Jena managed Land”) is designed to simulate vegetation composition and distribution as well as stocks and land-atmosphere exchange flows of carbon and water, for both natural and agricultural ecosystems. Using a combination of plant physiological relations, generalized empirically established functions and plant trait parameters, it simulates processes such as photosynthesis, plant growth, maintenance and regeneration losses, fire disturbance, soil moisture, runoff, evapotranspiration, irrigation and vegetation structure.


Levels of global warming

A specific level of global mean temperature compared to a reference period, which is usually representative of pre-industrial conditions. For example, the current mean temperature is 0.8°C - 1°C degrees warmer as compared to the year 1800.


The Model of Agricultural Production and its Impact on the Environment (MAgPIE) is a global land use allocation model, which is connected to the grid-based dynamic vegetation model LPJmL, with a spatial resolution of 0.5°x0.5°. It takes regional economic conditions such as demand for agricultural commodities, technological development and production costs as well as spatially explicit data on potential crop yields, land and water constraints (from LPJmL) into account. Based on these, the model derives specific land use patterns, yields and total costs of agricultural production for each grid cell. The objective function of the land use model is to minimize total cost of production for a given amount of regional food and bioenergy demand. Regional food energy demand is defined for an exogenously given population in 10 food energy categories, based on regional diets. Future trends in food demand are derived from a cross-country regression analysis, based on future scenarios on GDP and population growth.



MATSIRO is the land surface scheme of an Atmospheric Ocean General Circulation Model, the Model for Interdisciplinary Research On Climate, jointly developed by the Atmosphere and Ocean Research Institute at the University of Tokyo, the National Institute of Environmental Studies, and the Frontier Research Center for Global Change in Japan.



The median is the middle number in a list of numbers. Half of the numbers is higher than the median and half of the numbers are lower than the median.


MIROC5 is an atmosphere-ocean general cirulaton model developed by the Atmosphere and Ocean Research Institute (AORI) in Japan and the Japan Agency for Marine-Earth Science and Technology (JAMSTEC). The atmosphere model is the atmospheric general circulation model developed by CCSR–NIES–Frontier Research Center for Global Change, which is based on a global spectral dynamical core and includes a standard physics package. The ocean model is the CCSR Ocean Component Model, which includes a sea ice model. A land model that includes a river module is also coupled.

Watanabe, Masahiro, et al. “Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity.” Journal of Climate 23.23 (2010): 6312-6335.

Model ensemble

When a group of models is run with the same input, this is called a model ensemble. This is often done to get a grasp of the uncertainy of the models. A large difference between the models indicate a high uncertainty, while small diferences indicate a low uncertainy.

Modelling uncertainty

Uncertainty is 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 behaviour. 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).


Oxidative stress

Oxidative stress is the overproduction of reactive of free radical oxygen in plants which causes damage to the plant.

Demidchik, Vadim. “Mechanisms of oxidative stress in plants: from classical chemistry to cell biology.” Environmental and experimental botany 109 (2015): 212-228.


Ground that remains completely frozen—at 0°C or colder—for at least two years straight. These permanently frozen grounds are most common in regions with high mountains and Earth’s higher latitudes—near the North and South Poles.


A plaintiff is the party who initiates a lawsuit before a court

Plant physiology

The study of the physical, chemical and biological functioning of plants


PCR-GLOBWB 2 is a grid-based global hydrology and water resources model devloped at Utrecht University. The computational grid has a 5 arc-minute resolution (~10 km at the equator) and covers all continents except Greenland and Antarctica. Time steps for hydrology and water use are one-day, while the internal time stepping for hydrodynamic river routing is variable. For each grid cell and each time step, PCR-GLOBWB 2 simulates moisture storage as well as the water exchange between the soil, atmosphere and underlying groundwater reservoir. The exchange with the atmosphere comprises precipitation, evaporation from open water, snow and soils and plant transpiration, while the model also simulates snow accumulation, snowmelt, and glacier melt. PCR-GLOBWB simulates runoff partitioned into surface runoff, interflow, and groundwater recharge as well as routing of water over the terrain. Runoff generated by snow and glacier melt, surface runoff, interflow, and groundwater is routed across the river network to the ocean or endorheic lakes and wetlands using the kinematic wave approximation. It also possible to include floodplain inundation and to simulate surface-water temperature. PCR-GLOBWB 2 includes over 6000 manmade reservoirs from the GranD database that are progressively introduced in time and a reservoir operation scheme dependent on each reservoir’s purpose. Human water use is fully integrated with the hydrological model. Thus, at each time step: 1) water demand is estimated for irrigation, livestock, industry, and households; 2) these demands are translated into actual withdrawals from groundwater and surface water (rivers, lakes, and reservoirs) subject to availability of these resources and maximum groundwater pumping capacity in place; and 3) consumptive water use and return flows are calculated per sector. As an option PCR-GLOBWB 2 can be partially or fully coupled to a 2-layer global groundwater model based on MODFLOW and to the hydrodynamic model codes DFLOW-FM, CaMaFlood and LISFLOOD-FP.

Also see global hydrological models


Potential evapotranspiration

The magnitude of the transfer of water from land to atmosphere by means of evaporation and transpiration by plants if sufficient water were available.


Precipitation is the sum of rainfall and snowfall.

Pre-industrial climate

The behaviour of Earth’s climate before human activity caused large-scale emissions of greenhouse gases. Variations of climate here are internal-variability driven.


A projection is an estimate or forecast of the future based on a set of assumptions. For example, given that countries reduce their CO2 emmissions by x%, the global temperature increase will be limited to 2 degrees Celcius.

Relative risk (RR)

Measure widely used in epidemiology to estimate the association between an exposure (e.g., heat) and an outcome (e.g., death). It is defined as the ratio of the probability of the outcome in the exposed group P1P_1 to the probability of the outcome in the unexposed group P0P_0 , i.e. RR=P1/P0RR = P_1 / P_0 .

Return time

The highest or lowest value of a given variable, on average occurring once in a given period of time (e.g., in 10 years). For example, a 10-year return level for of 40 degrees indicates that a 40 degrees is attained or exceeded only every 10 summers on average.

Representative lake

ISIMIP simulations are performed at a 0.5°x0.5° latitude-longitude resolution. However, a 0.5°x0.5° pixel potentially contains multiple lakes with different characteristics (e.g., in terms of bathymetry, transparency, fetch), and it is not possible to fully represent this subgrid-scale heterogeneity. Instead, the global-scale lake simulations provide a ‘representative lake’ for a given pixel, which uses the average lake depth and area of real lakes inside the pixel according to the Global Lake Data Base.


A low emmission Representative Concentration Pathway where radiative forcing peaks at approximately 3 W/m2 and then declines to be limited at 2.6 W/m2 in 2100 (the corresponding Extended Concentration Pathway, or ECP, has constant emissions after 2100).



A middle of the road Representative Concentration Pathway where radiative forcing peaks at approximately 4.5 W/m2 in 2100 (the corresponding Extended Concentration Pathway, or ECP, has constant emissions after 2150).



A high emission Representative Concentration Pathway where radiative forcing peaks at approximately 6.0 W/m2 in 2100 (the corresponding Extended Concentration Pathway, or ECP, has constant emissions after 2150).



A very high emission Representative Concentration Pathway which leads to >8.5 W/m2 in 2100 (the corresponding ECP has constant emissions after 2100 until 2150 and constant concentrations after 2250).


Representative Concentration Pathways (RCPs)

Scenarios that include time series of emissions and concentrations of the full suite of greenhouse gases (GHGs) 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 the fact that not only the long-term concentration levels but also the trajectory taken over time to reach that outcome are of interest (Moss et al., 2010). RCPs were used to develop climate projections in CMIP5.


Moss, R., et al., 2008: Towards new scenarios for analysis of emissions, climate change, impacts and response strategies. Intergovernmental Panel on Climate Change, Geneva, 132 pp.

Moss, R. et al., 2010: The next generation of scenarios for climate change research and assessment. Nature, 463, 747–756.

River outlet

Also called pour point, is the point on the surface at which water flows out of a watershed.

Rootzone soil moisture

Water that is available in those parts of the soil from which plant roots can extract water, thereby being an important limiting factor constraining crop productivity.


The flow of water over the surface or through the subsurface, which typically originates from the part of liquid precipitation and/or snow/ice melt that does not evaporate or refreeze, and is not transpired.


Shared Socioeconomic Pathways (SSPs)

The Shared Socioeconomic Pathways (SSPs) will be used alongside the Representative Concentration Pathways (RCPs) to analyze the feedbacks between climate change and socioeconomic factors, such as world population growth, economic development and technological progress.



Accumulation of layers of snow


Sustainable Shared Socioeconomic Pathway.

This is a world making relatively good progress towards sustainability, with sustained efforts to achieve development goals, while reducing resource intensity and fossil fuel dependency. Elements that contribute to this are a rapid development of low-income countries, a reduction of inequality (globally and within economies), rapid technology development, and a high level of awareness regarding environmental degradation. Rapid economic growth in low-income countries reduces the number of people below the poverty line. The world is characterized by an open, globalized economy, with relatively rapid technological change directed toward environmentally friendly processes, including clean energy technologies and yield-enhancing technologies for land. Consumption is oriented towards low material growth and energy intensity, with a relatively low level of consumption of animal products. Investments in high levels of education coincide with low population growth. Concurrently, governance and institutions facilitate achieving development goals and problem solving. The Millennium Development Goals are achieved within the next decade or two, resulting in educated populations with access to safe water, improved sanitation and medical care. Other factors that reduce vulnerability to climate and other global changes include, for example, the successful implementation of stringent policies to control air pollutants and rapid shifts toward universal access to clean and modern energy in the developing world.



Middle of the Road Shared Socioeconomic Pathway.

In this world, trends typical of recent decades continue, with some progress towards achieving development goals, reductions in resource and energy intensity at historic rates, and slowly decreasing fossil fuel dependency. Development of low-income countries proceeds unevenly, with some countries making relatively good progress while others are left behind. Most economies are politically stable with partially functioning and globally connected markets. A limited number of comparatively weak global institutions exist. Per-capita income levels grow at a medium pace on the global average, with slowly converging income levels between developing and industrialized countries. Intra-regional income distributions improve slightly with increasing national income, but disparities remain high in some regions. Educational investments are not high enough to rapidly slow population growth, particularly in low-income countries. Achievement of the Millennium Development Goals is delayed by several decades, leaving populations without access to safe water, improved sanitation, medical care. Similarly, there is only intermediate success in addressing air pollution or improving energy access for the poor as well as other factors that reduce vulnerability to climate and other global changes.



Fragmentated Shared Socioeconomic Pathway.

The world is separated into regions characterized by extreme poverty, pockets of moderate wealth and a bulk of countries that struggle to maintain living standards for a strongly growing population. Regional blocks of countries have re-emerged with little coordination between them. This is a world failing to achieve global development goals, and with little progress in reducing resource intensity, fossil fuel dependency, or addressing local environmental concerns such as air pollution. Countries focus on achieving energy and food security goals within their own region. The world has de-globalized, and international trade, including energy resource and agricultural markets, is severely restricted. Little international cooperation and low investments in technology development and education slow down economic growth in high-, middle-, and low-income regions. Population growth in this scenario is high as a result of the education and economic trends. Growth in urban areas in low-income countries is often in unplanned settlements. Unmitigated emissions are relatively high, driven by high population growth, use of local energy resources and slow technological change in the energy sector. Governance and institutions show weakness and a lack of cooperation and consensus; effective leadership and capacities for problem solving are lacking. Investments in human capital are low and inequality is high. A regionalized world leads to reduced trade flows, and institutional development is unfavorable, leaving large numbers of people vulnerable to climate change and many parts of the world with low adaptive capacity. Policies are oriented towards security, including barriers to trade.



Inequal Shared Socioeconomic Pathway.

This pathway envisions a highly unequal world both within and across countries. A relatively small, rich global elite is responsible for much of the emissions, while a larger, poorer group contributes little to emissions and is vulnerable to impacts of climate change, in industrialized as well as in developing countries. In this world, global energy corporations use investments in R&D as hedging strategy against potential resource scarcity or climate policy, developing (and applying) low-cost alternative technologies. Mitigation challenges are therefore low due to some combination of low reference emissions and/or high latent capacity to mitigate. Governance and globalization are effective for and controlled by the elite, but are ineffective for most of the population. Challenges to adaptation are high due to relatively low income and low human capital among the poorer population, and ineffective institutions.



Shared Socioeconomic Pathway with conventional development.

This world stresses conventional development oriented toward economic growth as the solution to social and economic problems through the pursuit of enlightened self interest. The preference for rapid conventional development leads to an energy system dominated by fossil fuels, resulting in high GHG emissions and challenges to mitigation. Lower socio-environmental challenges to adaptation result from attainment of human development goals, robust economic growth, highly engineered infrastructure with redundancy to minimize disruptions from extreme events, and highly managed ecosystems.


Soil moisture

Water stored in the soil in liquid or frozen form. Rootzone soil moisture is of most relevance for plant activity


Soil water column

Water that is available within the full depth of the modelled 3D column that is representing the soil within the impact models.


A plant’s stomata are pores found on leaves that enable plants to exchange gasses with the atmosphere, such as water vapor, CO2 and oxygen.

Target variable (detection and attribution)

The most publicly known target variable would be our global average temperature, which is discussed in reference to our mitigation targets. However, target variables are not limited to their global average outlook, as this scope can ignore regional differences in the behaviour of a variable. As well, they are not limited to temperature indices. Other target variables that have been assessed in detection and attribution studies are precipitation and non-atmospheric variables like soil moisture or river discharge.

Time series

A time series is a series of data points indexed, listed or graphed in temporal order.


The process by which plants give off water vapor through openings in their leave pores or stoma.


WaterGAP2 is a global water availability and water use model. WaterGAP2 is one of the 13 global hydrology models following the ISIMIP2a protocol which form the base of simulations for the ISIMIP2a global water sector outputs.

Also see global hydrological models.



An area of land that drains or “sheds” water into a specific waterbody, such as the ocean, another river or even a lake.


The World Meteorological Organization (WMO) is a specialized agency of the United Nations promoting interna-tional cooperation in the field of meteorology, hydrology and related geophysical sciences.


Hegerl, G. C., F. W. Zwiers, P. Braconnot, N. P. Gillett, Y. Luo, J. A. Marengo Orsini, N. Nicholls, J. E. Penner, and P. A. Stott. 2007. “Understanding and Attributing Climate Change.” Book Section. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group i to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H. L. Miller. Cambridge, United Kingdom; New York, NY, USA: Cambridge University Press.
Muñoz-Sabater, Joaquı́n, Emanuel Dutra, Anna Agustı́-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, et al. 2021. “Era5-Land: A State-of-the-Art Global Reanalysis Dataset for Land Applications.” Earth System Science Data 13 (9): 4349–83.