Climate Information Basics

Sea Surface Temperature (2005); NASA Scientific Visualization Studio


Climate information is needed to characterize climate risks and to boxinform  decision-making for effective risk management. However, decision makers often have to make do with limited availability of climate information and limited capacity to apply such information. Moreover, in the context of climate change adaptation the selection of future climate scenarios has become much richer and global climate model (GCM) outputs can now be used to create regional and local climate projections. 

Several guides have been developed to help decision makers and information providers understand and utilize climate information required for adaptation planning. However, in many developing regions climate data are sparse and the provision of climate information does not follow well-established guidelines. Furthermore, to understand how the climate may change, it is first important to have a good understanding of baseline climatic conditions; spatial and temporal data gaps can make this challenging (see Climate Data Types). 

This section reviews key concepts necessary for better understanding available climate information and its limitations, and for more effective communication with information producers.  It draws on many of the articles and reports listed in the Resources section.


What is climate change?

Framing Information Needs


In general, climate information needs will depend on the scope of the  adaptation planning/analysis, ranging from initial risk screening and detailed risk analysis to assessing risk management options. There is also a need to clearly identify non-climate drivers. However, when seeking climate information, practitioners need to clearly understand the statistical nature of climate change, the fundamental difference between data and information as well as between climate vs. impact information, and the need to account for uncertainties in the projections. In addition, practitioners need to clearly articulate their needs around specific time horizons (periods of time) and temporal and spatial resolution.

The Statistical Nature of Climate Change

While the concept of climate change may connote modifications to something steady and immutable, climate in fact varies on a number of scales. Human activities as well as natural systems evolve in response to such variations and can cope with a range of climatic conditions without serious damage. Figure 1.1 conceptualizes current climate variations and the coping range. Occasionally, climate variations exceed the coping range and the system is put under higher stress or severely damaged.  If the frequency of such events is low, the system will progressively recover. When they become more frequent, the system may reach a new state. Within such a framework, climate change means that severe impact events may happen more frequently, and/or events of unprecedented magnitude may occur, putting the system under such stress that recovery is difficult if not impossible (see figure 1.1). 

Figure 1.1 Conceptual illustration of historical and future climate, coping range and adaptation. Source: Lu, 2007, adapted from Carter et al. 2007.
Figure 1.2 The effect of changes in temperature distribution on extremes. Different changes in temperature distributions between present and future climate and their effects on extreme values of the distributions: (a) effects of a simple shift of the entire distribution toward a warmer climate; (b) effects of an increase in temperature variability with no shift in the mean; (c) effects of an altered shape of the distribution, in this example a change in asymmetry toward the hotter part of the distribution. Source: IPCC (2012)

Adaptation is aimed at limiting damage and speeding recovery from such damaging events. As seen in figure 1.2, high impact events in the future can occur more frequently due to:

  • Change in the average, keeping the amplitude of the variability the same (Fig. 1.2a) 
  • Change in the amplitude of the variability, keeping the average the same (Fig. 1.2b)
  • Change in the frequency of rare events on one side of the distribution (Fig. 1.2c)
  • Any combination of the above at the same time.


Data vs. Information

Decision makers needing climate information must understand the distinction between data and information. Very often requests are made for ‘data’ while what is needed is ‘information.’ Data is simply an array of numbers, most often recorded values of a meteorological variable in a given location with a given time-step; for example, daily rainfall recorded in Accra. As such, data may not be very useful to policy-makers and practitioners, unless they are used in impact models or other types of data analyses. 

Most of the time, these audiences need information extracted from the data: average annual rainfall, rainfall seasonality, trends in temperature, frequency of dry spells lasting longer than a given threshold, or frequency of temperature exceeding a threshold. Information is extracted from the data with a given objective in mind and will differ from sector to sector and from hazard to hazard. Average temperature in a location as well as the frequency of heat waves are both derived from the same data, applying different statistical analyses. However, while it may seem desirable for each sector and practitioner to derive their own information from the data, in practice, it is best to ask data holders (Meteorological Services) to provide this information because they are most aware of data limitations. For example, the quality of rainfall data in a given location might be sufficient to estimate the average annual rainfall but not good enough to estimate the frequency of extreme rains. Therefore, it is best to approach information producers to directly obtain the information rather than ask for the raw data. This also avoids duplication of datasets as well as issues with dataset versions and updates.

Climate Information vs. Impacts Information

Another useful distinction to keep in mind is between climate and impact information. Diverse past and projected statistics of meteorological data, as cited above and applied to rainfall, temperature, atmospheric moisture, solar radiation, and wind are climate information and should be available from Meteorological Services. Changes in crop yields due to variations in temperature, forecasts of areas flooded in a flash flood after heavy rain or changes in vector-borne disease incidence due to variations in temperature and rainfall, are usually beyond the mandate of climate information producers. Such impact information is usually developed by scientists in collaboration with sectoral decision makers (e.g., disaster managers, agricultural or health ministries) using impact models (e.g., crop models, hydrological or flood models, vector and disease models). In addition to many local or regionally calibrated models, a wide range of global impact models are now available through the Inter-Sectoral Impacts Model Intercomparison Project (ISI-MIP).


Adaptation practitioners need to accept that information about future climate is provided with a certain level of uncertainty. This is inherent to any model of the real world, and especially to projections of the future. Uncertainty needs to be factored into the decision-making process. Uncertainty does not mean that future climate is totally unknown or that projections are false. Moreover, uncertainty can be quantified and decisions can still be made. There are four main sources of uncertainty in climate projections:

  1. Future levels of anthropogenic emissions and occurrence of natural phenomena (e.g. volcanic eruptions)
  2. Imperfections of the climate models used to project changes in climate
  3. Imperfect knowledge of current climate that serves as starting point for the projections
  4. Difficulty in representing, thus reliably projecting, interannual and decadal variations in climate

Time Horizon

The type of information needed will depend on the type of adaptation intervention or investment proposed, and the return period over which the value of the investment is considered.  Figure 1.3 presents examples of different decision types as a function of their time horizon. Larger infrastructure investments (irrigation, transportation network, dams, etc.) are usually considered over longer time horizons than individual decisions/investments such as cropping portfolios or farm planning. The time horizon of the intervention or investment will impact the precision with which the information can be provided. While the amount of rainfall or the probability of a long dry spell in the coming rainy season can be predicted with a higher degree of accuracy, only general tendencies in rainfall, with a wide uncertainty, can be provided for a horizon of 30 to 50 years. In addition, farming systems 50 years from now will most probably result from successive adaptation stages rather than from changes planned 50 years ahead of time. Not all of these changes will be driven by climate; changes in technology, seed stocks, global and local markets, and customer preferences will all play significant roles.

Figure 1.3 Adaptation decision contexts and their associated time horizons. Source: Lu. 2007

Thus, in a lot of cases interventions other than large scale infrastructure investments will focus on much shorter time horizons, more compatible  with decisions at individual and community levels and project cycles. At such horizons, changes in climate might be less important than interannual variability, and even not significant. In addition, IPCC (2012) recommends focusing on measures that provide benefits both for future climate conditions and for current levels of climate variability, since current extremes are often a foretaste of future climate characteristics. These so-called “no regret strategies” have the potential to offer benefits now and lay the foundation for addressing changes in the future. With respect to Figure 1.1, such approaches expand the current coping range, irrespective of the direction that climate change is going to take.


Figure 1.4 presents a more complete continuum of horizons, including shorter time scales, and related adaptation applications. Note that the ability to provide climate information ahead of time for horizons shorter than several decades derives from drivers that are different from anthropogenic emissions, and that different time-scales have different drivers and different data needs. Thus, the issue of adaptation to climate change requires focusing on data collection and climate information not only for the distant future. It is useful to differentiate between time-frames below.

Figure 1.4 Schematic diagram showing the relationship between climate timescales, from weather to climate change, and emergency adaptation mechanisms, from relief operations to climate change planning. Source: Mason et al. 2015, adapted from WMO(n.d)


Time Frames

Most adaptation practitioners require information about climate patterns in the future in order to develop adaptation plans. However, climate information from the past and information on  current climate variability are equally important for adaptation planning. Historical climate records allows practitioners to assess the frequency and magnitude of catastrophic events that negatively affect communities, livelihoods, and ecosystems.  These assessments can constitute a baseline against which future changes in climate can be assessed. Conversely, current climate monitoring can facilitate the development of effective Early Warning Systems and support building resilience in communities. 

Historical Climate Information

Historical climate analysis is a valuable component of adaptation assessments because it serves as a foundation for understanding current and future climate. Data on past conditions and trends can be used for mapping hazards, identifying relationships with past impacts (disease outbreaks, food crisis, etc.) and assessing relevant thresholds in climatic variables. It also provides a reference against which current and future conditions can be compared. Finally,  an understanding local climate characteristics such as seasonality and related disease or agricultural cycles or the coping range (Figure 1.1) and patterns of natural variability can be built from historical observations. 

It is particularly important to distinguish three different time-scales of climate variability and their relative importance to the given application.

  • Interannual variability: changes in rainfall amounts and temperature for a given month or season from one year to the other. This is an intrinsic part of climate and such variations occur because of interactions between different components of the climate system. Socio-economic activities as well as ecosystems are usually adapted to a certain degree to such variations and it is only a few extreme years/seasons that lead to impacts beyond the coping range of human and natural systems (Figure 1.5).
  • Decadal variability refers to the occurrence of a sequence of drier than   
    Figure 1.5 Different scales of variability in the climate system: (a) variability in temperature in South Africa, decomposed into (b) climate trend, (c) decadal variability, and (d) interannual variability.

    normal or wetter than normal years over periods of 10 to 30 years. Such sequences of repeated droughts for example have profound consequences on ecosystems and socio-economic activities as the systems often do not have time to recover before facing another event. The sequence of the Sahelian droughts in the 1970s and 1980s is the most notable example of such decadal variability in rainfall (Figure 1.6).  Decadal variability was also detected in East Africa, Atlantic hurricane activity and the Dust Bowl In the USA. It is critical to assess the importance of the decadal variability in the region of interest as it may impact the detection of longer term trends and may lead to an erroneous attribution of drier/wetter conditions to climate change (based on extrapolation of the trends).

  • Long-term trends refer to climate shifts (beyond 30 years) with a new long-term mean and interannual variations occurring around that new mean, with possibly a different amplitude (Figure 1.1). The most important contributor to such shifts currently is anthropogenic climate change. However, it is important to note that such long term trends are most easily detected in temperature while in rainfall long term changes or trends are often masked by interannual and decadal variability. Thus, in regions where water/rainfall are the limiting factors, reduction of vulnerabilities to interannual and decadal variations might be more important than adaptation to potential changes 50 to 80 years into the future.
Figure 1.6 The Standardized Precipitation Index for 12 months (SPI-12) and its 11-year running mean for (a) the Sahel and (b) the Guinea Coast between 1921 and 2010, based on records in approximately 76 stations across West Africa for the period 1921-2010 and over 300 stations over the period 1980-2010. Source: Sanogo et al., 2015.

Analysis of the historical climate can be performed at various spatial scales depending on data availability and  the scope of the analysis. In an optimal scenario, long time-series of climate data from several meteorological (met) stations within a country should be used for the analysis. Met station data is the most accurate source of climate information and it reflects local conditions, capturing spatial variations within the country, which is important for national adaptation strategies. Historical climate analyses include long-term assessments of averages and variability, including statistics on extreme events and overall trends. They can also include change-point analysis (Lanzante, 1996) i.e.,  the detection of a point in time where the time series changes its characteristics (mean, distribution etc.).  Figure 1.7 provides an illustrative example of a time series that has undergone change-point analysis. The change point is circled and the two time periods are clearly distinguished by their differences in mean. 

Figure 1.7 Time series anomaly of monthly hPa geopotential height anomaly (m) at Hong Kong from 1950-1960. The monthly anomaly values are connected by a curve. The horizontal lines represent the biweight means of the two segments defined by the change-point (circled star). Source: Lanzante, 1996.

Current Climate Monitoring & Early Warning Systems 

  • Climate monitoring is the assessment of near real-time climate information and comparison with historical information. Monitoring  boxproducts will depend on the decisions they support but most frequently they include information on the previous ten days, previous month or previous three-month period. In addition to presenting values and anomalies they are also often presented in the context of historical information; e,g., with respect to standard deviation or thresholds of extreme events or in terms of return periods. While often used to qualify the amplitude of extreme events, such as extreme rainfall or a heat wave, climate monitoring is also instrumental in early identification of the slow-onset events. Climate monitoring is an essential element of Early Warning Systems.
  • Early Warning Systems (EWS) enable timely responses to both short-term/rapid (e.g. cyclones, floods and storms) and long-term/slow (e.g. drought and long-term climate change) onset climatic hazards. An EWS is an integrated method of hazard monitoring, forecasting and prediction, disaster risk assessment, communication and preparedness that enables individuals, communities, governments, businesses and others to take timely action to reduce disaster risks in advance of hazardous events (UNDP, 2015). Effective EWSs often require a joint effort among various national agencies to deliver early warnings in a timely and effective manner and their ultimate effectiveness and accuracy depend on climate information. They can be viewed as part of resilience building or adaptation strategies.

Predicting the Future: from Weather Forecasts to Climate Projections


Forecasts, projections and scenarios are useful for anticipating climate  hazards, for planning humanitarian operations, and for long-term recovery and development planning. 

  • Forecasts are specific statements about the expected conditions for the immediate or very near future (up to a few months). Weather forecasts provide information about specific conditions in a specific place and time, typically up to a few days in advance. Conversely, climate forecasts are typically offered for the upcoming season and only give general tendencies of the climate over larger spatial scales (e.g., probabilities for above or below normal precipitation). These forecasts can be  are incorporated into Early Warning Systems, and together with the weather and climate monitoring provide a basis for issuing alerts. Both weather and climate forecasts should be accompanied by an assessment of their accuracy in the past and present estimates of uncertainty.
  • Projections are statements about climate conditions much further into the future, typically more than one year. Climate change projections usually target temporal scales of 30 to 50 years.  Other projections can also be made—most notably to address decadal variations—though these are currently considered more theoretical than operational.
Figure 1.8 Long-term time series (1970-2100) of seasonal (May-September) mean temperature (left panels) and precipitation (right panels) anomalies for the Sahel (upper panels), the Gulf of Guinea (middle panels) and the West Africa (lower panels) and for both RCP4.5 and RCP8.5 based on multimodel CORDEX simulations. The anomalies are calculated with respect to the seasonal mean of the period 1976-2005. The shaded areas denote ensemble maxima and minima. (a) Sahel temperature change. (b) Sahel precipitation change. (c) Guinea temperature change. (d) Guinea precipitation change. (e) West Africa temperature change. (f) West Africa precipitation change.

Forecasts and projections can be presented as:

  • Deterministic: one possible outcome with no information about possible errors in forecast.
  • A range of values: an upper and lower limit between which actual values are expected to occur. This format attempts to account for potential different outcomes but does not state the likelihood of each outcome. Graphs of the spread in projected temperature or rainfall are typical of this format (Figure 1.8)
  • Probabilities: different probabilities are given for different outcomes/values/ranges. Such a format accounts for known errors and uncertainties in the forecasts/projections.

Time and Space Resolution

Temporal Resolution

It is important to understand that for some assessments or sectors average climatic conditions, such as average annual or seasonal rainfall, are sufficient while for othersespecially the ones dealing with extreme weather eventsanalysis of daily records is necessary. The data acquisition process and data quality make different time resolutions more or less reliable. For example, if large gaps in daily rainfall records exist, it may still be possible to derive a reliable estimate of the average annual rainfall while a reliable analysis of the extreme rainfall may not be possible. 

Spatial Resolution

The spatial resolution of the information needed is defined by the purpose of the assessment. A global assessment of food security can rely on global coarse resolution gridded data and model outputs, on the order of several hundreds of kilometers. If the information is needed to design localized land use plans or delineate flood prone areas, then information at much finer scale (a few hundreds of meters) is needed. Similarly, climate information might be required for a variety of spatial extents, from regional to country to water basin to individual farm, and addressing those different extents involves using data at different spatial resolutions. Coarser resolution information is usually more easily available but will not capture climate variability or differences over short distances over heterogeneous landscapes (e.g., mountain areas).

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