Frequently Asked Questions

Here is a wonderful page to help respond to climate change skeptics or deniers:


Climate models

Global climate models or General Circulation models (GCMs) are the most complex and precise models for understanding climate systems and predicting climate change. These models aim to mathematically describe the Earth’s climate system based on the laws of physics (e.g. first law of thermodynamics, Stefan-Boltzmann law), fluid motion (e.g. Navier-Stokes equations) and chemistry. They use mathematical equations to quantify observable Earth system processes, i.e. characterize how energy and matter interact and get transported in different parts of the atmosphere, land, ocean and sea ice (Fig. 1).

The atmospheric component of the climate model simulates clouds, aerosols and the transport of heat and water around the globe. The land surface component simulates surface characteristics such as vegetation, snow cover, soil water, rivers, and carbon storing, whereas the ocean component simulates current movement, mixing and ocean biogeochemistry. The sea ice component modulates solar radiation absorption, air-sea heat and water exchanges.

Building a complex global climate model incorporating all these components requires the division of the Earth’s surface into three dimensional grid cells (Fig. 1). The size of these grid cells defines the spatial resolution of the model (typically about 100 km x 100 km x 30 vertical layers). Climate models also incorporate the dimension of time, measured in time steps. The temporal resolution refers to the size of these time steps (typically about 30 minutes) used in the model. Powerful supercomputers iteratively solve the mathematical equations for every single spatial grid cell and step the model forward in time to produce a precise climate model for a specific time interval. Models with smaller grid cells as well as smaller time steps lead to better resolution, but also need considerably more computing power.

Fig. 1: Schematic representation of GCMs (https://www.gfdl.noaa.gov/climate-modeling/)



The main inputs for a climate model are external factors, so called “forcings”, that change the amount of the sun’s energy absorbed by the Earth or trapped in the atmosphere. Examples of these forcings are the sun’s varying radiation output, variable atmospheric concentrations of greenhouse gasses (e.g. CO2, methane, N2O) or aerosols (particles emitted e.g. by fossil fuel burning and volcanic eruptions influencing sunlight and cloud formation). These factors are incorporated into the climate model as best estimates of past conditions or as part of future socio-economic and emission scenarios.

Past forcings can be estimated by reconstructing ancient greenhouse gas concentrations (e.g. by analyzing air trapped in ice cores), climate gas and particle emissions during past volcanic eruptions or changes in the Earth’s orbit (i.e. cyclical variations in solar radiation reaching the Earth due to Milankovitch cycles).

Concerning future forcings, different scenarios of future developments in technology, energy and land use provide potential  pathways, so called “Representative Concentration Pathways” (RCPs), for atmospheric greenhouse gas concentrations (Fig. 1).

Fig. 1: Future trends in concentrations of greenhouse gases based on different RCP scenarios assuming different amounts of radiative forcing (van Vuuren et al., 2011).

The main outputs for a climate model are normally temperatures and humidity of different atmospheric layers from the surface to the upper stratosphere. Climate models also produce estimates of ocean temperatures, salinity and pH from the surface to the seafloor as well as snowfall, rainfall, snow cover and the extent of glaciers, ice sheets and sea ice. They also give information about wind speed, strength and direction, as well as climate features, such as the jet stream and ocean currents. “Climate sensitivity” can also be modelled (i.e. the warming expected when the concentration of carbon dioxide in the atmosphere reaches twice the amount it was in preindustrial times).


van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K., 2011, The representative concentration pathways: an overview: Climatic Change, v. 109, no. 1, p. 5.

Climate models are tested by comparison of model predictions with real-world observations. For this purpose, climate models are run over a historical period, from around 1850 to near-present, using best estimates for the past forcings  during this time period (also see “What are the inputs and outputs for a climate model?”). These “hindcasts” of the past climate (e.g. surface temperatures)are then compared to actual recorded climate observations (Fig. 1c). The more precise the hindcast of past climate, the more reliable is the climate model, also in forecasting future climate.

These historical “hindcast” runs can also be used to determine human influence on climate change, the so called “attribution”. For this purpose, models are run with either only natural forcings (e.g. solar variation and volcanic activity) or anthropogenic forcings (e.g. greenhouse gasses and aerosols) as model inputs (Fig. 1a, b). These graphs show that natural forcings alone can’t explain climate’s behavior. Only when we also take anthropogenic forcing into account, we can explain the observed climate patterns.

Fig. 1: Comparison of model results with recorded climate observations: (a) climate model with only natural forcings as input, (b) climate model with only anthropogenic forcing as input, (c) climate model with both natural and anthropogenic forcings as input (https://www.ipcc.ch/report/ar3/wg1/summary-for-policymakers/spmfig04/).

Furthermore, big perturbation events like volcanic eruptions can be used to test climate model performance. Model projections can be compared to recorded short-term climate responses after an eruption. Studies focusing on the Mount Pinatubo eruption show that models can accurately project changes in temperature (Hansen et al., 1996) and atmospheric water vapor (Soden et al., 2002).

To produce more reliable estimates of twenty-first century climate, climate models are also tested against paleoclimate data (reaching back up to 21,000 years). This data shows larger climate changes than the observational record of the last 150 years, against which climate models are normally evaluated. Ice-core, marine (e.g. marine sediments) and terrestrial archives (e.g. tree rings) provide information about environmental responses to past climate changes. These records can be used to derive estimates of climate, i.e. provide paleo-proxies for past climate (e.g. paleotemperatures). Thus, the geologic record provides a unique opportunity to test model performance outside of the comparison with the short-term observational record. Evaluation of model simulations against paleodata shows that models reproduce the direction and large-scale patterns of past changes in climate, even though they tend to underestimate the magnitude of regional changes (Braconnot et al., 2012).


Braconnot, P., Harrison, S. P., Kageyama, M., Bartlein, P. J., Masson-Delmotte, V., Abe-Ouchi, A., . . . Zhao, Y. (2012). Evaluation of climate models using palaeoclimatic data. Nature Climate Change, 2(6), 417-424.
Hansen, J., Sato, M., Ruedy, R., Lacis, A., Asamoah, K., Borenstein, S., . . . Campbell, M. (1996). A Pinatubo climate modeling investigation. In The Mount Pinatubo Eruption (pp. 233-272): Springer.
Soden, B. J., Wetherald, R. T., Stenchikov, G. L., and Robock, A., 2002, Global Cooling After the Eruption of Mount Pinatubo: A Test of Climate Feedback by Water Vapor: Science, v. 296, no. 5568, p. 727-730.

Modern climate models can generally be considered reliable tools for predicting climate. A recent study (Hausfather et al., 2020) evaluated the performance of various climate models published between the early 1970s and the late 2000s. They looked at how well models project global warming in the years after they were published by comparing the model projections to actual observed temperature changes. 14 out of the 17 model projections were consistent with observation, especially when mismatches between projected and observationally-informed estimates of forcing were taken into account. This means that the actual climate physic models were generally accurate and mismatches between output model temperatures and observed climate data occurred mainly due to uncertainties in future forcing estimates, i.e. estimates of future climate gas emissions, which need to be put into the climate model (also see “What are the inputs and outputs for a climate model?”).


Hausfather, Z., Drake, H. F., Abbott, T., and Schmidt, G. A., 2020, Evaluating the Performance of Past Climate Model Projections: Geophysical Research Letters, v. 47, no. 1, p. e2019GL085378.

As computational power is limited, there is a lower limit to the grid cell size for which climate models can be calculated (see also “What is a climate model?”). However, there are processes at scales below the model’s spatial resolution (normally around 100 x 100 km), e.g. clouds, convection in the atmosphere, eddies in the ocean, land surface processes (Fig. 1). The physics of these processes needs to be “parameterized”. These parameterizations are approximations of the specific phenomena to be modelled, at the scales the model can actually resolve. Parameterization is also used as an approximation of climate processes which are not yet fully understood. Parameterizations are the main source of uncertainty in climate models.

Fig. 1: Climate processes and properties that typically need to be parameterized within global climate models (MetEd, The COMET Program, UCAR).

As our knowledge of the climate as well as our empirical observations are incomplete, we cannot always narrow down parameterized variables into a single value. Therefore, tests with the model are run. Estimations of parameterized variables are put into the model to find the value, or set of values, that give the best representation of the climate. This process is called “model tuning”. Modelers tune their models to ensure that the long-term average state of the climate is accurate – including factors such as absolute temperatures, sea ice concentrations, surface albedo and sea ice extent.

There are also some limitations associated with modelling climate at regional and local scales. To bridge the gap from the large spatial scales represented by GCMs to the smaller scales required for assessing regional climate change and its impacts, different downscaling methods are used. There are two ways of downscaling: regional climate models (RCMs) and empirical-statistical downscaling (ESD). Regional climate models (RCMs) take the low-resolution solution provided by the GCMs and include finer topographical details such as the influence of lakes, mountain ranges and a sea breeze to calculate more detailed information. These models can achieve a resolution of around 25km x 25km. As it is the larger scale model information that drives the finer-scale model, this approach only provides limited improvability of the data.

Empirical-statistical downscaling (ESD) is an alternative that does not require much computing power. ESD uses observed climate data to establish a statistical relationship between the global and local climate. According to this relationship, local changes can be derived based on the large scale projections coming from GCMs or observations.

Both RCMS and ESD give relatively consistent results with each other as well as with observed data (Fig. 2). However, RCMs as well as ESD downscaled information relies heavily on the quality of the information that it is based on, i.e. the observed data or the GCM data input. Downscaling only provides more location-specific data, it does not make up for any uncertainties that stem from the data or GCM it relies on.

Fig. 2: A comparison between RCM results based on different climate models (colored dots with error bars) and ESD results (red region showing the 90% confidence interval for the model ensemble), actual observations are shown as black symbols (Førland et al., 2011).

Global as well as downscaled climate models can simulate climate quite accurately, but sometimes they show substantial deviations from observed climate, known as “bias”, especially at the regional and local scale. Bias is defined as the systematic difference between a modelled climate property (e.g. mean temperature) and the corresponding real property. Bias correction can be applied to account for these differences. An empirical transfer function between simulated and observed climate properties is calibrated and applied to the model output data to match observational climate data. Bias correction is a mere post-processing and cannot fix problems with the actual climate model.

Individual climate models may also struggle to accurately depict natural climate variability, i.e. natural short-term fluctuations on seasonal or multi-seasonal time scales (e.g. North Atlantic Oscillation (NAO) or El Niño Southern Oscillation (ENSO)). However, when combining several independent models, this variability can be reduced. Averaging an ensemble of different climate models can produce forecasts, which show better skill, higher reliability and consistency in predicting climate (Hagedorn et al., 2005).

In conclusion, modern climate models can definitely provide reliable projections at larger, global scales. However, they reach their limits when having to deal with small scale processes at regional or local scale and short-term climate variability. To deal with these problems, there are some effective methods available (as described above). Even though models will never predict our climate system 100% accurately, they are definitely still skilled in giving us a reasonably precise prediction of future climate; or, to put it in George Box’s words: “All models are wrong, but some are useful”.


Førland, E. J., Benestad, R., Hanssen-Bauer, I., Haugen, J. E., and Skaugen, T. E., 2011, Temperature and precipitation development at Svalbard 1900–2100: Advances in Meteorology, v. 2011.
Hagedorn, R., Doblas-Reyes, F. J., and Palmer, T. N., 2005, The rationale behind the success of multi-model ensembles in seasonal forecasting – I. Basic concept: Tellus A, v. 57, no. 3, p. 219-233.

Understanding past, present and future climate and combining this knowledge with climate models helps to determine natural as well as man-made influences on the climate system of the past and the future (also see“How are climate models validated? How are they tested?”). Climate projections are helpful in assessing the impact of future climate change and assist decision-makers to prioritize environmental issues based on scientific evidence (Fig. 1).

Fig. 1: Climate change vulnerability (Wesleyan University and Columbia University).

Therefore, it is important to continue to collect data and improve recent models to increase their accuracy and refine our knowledge of the climate system. Climate models have the ability to influence the way communities and policy-makers plan for the future. These models are our best chance at finding ways to mitigate the dangerous effects of climate change.




Climate change affects each and every one of us. Its impacts are far-reaching, affecting potentially almost every aspect of our lives. Climate change has a direct effect on our health, our food and water sources, the air we breathe, and the weather we experience.

Climate change leads to extreme weather like hurricanes, floods, and wildfires and all the consequences these events carry with them. (link with detailed answer to this). Changing patterns of weather – more heat waves, droughts and altered precipitation patterns can lead to crop failures and food and water insecurity.

As climate continues to change, the risks to human health increase. For example, climate change promotes spreading vector-borne diseases as pests, such as mosquitos and ticks, expand their habitat and life cycles due to the rising temperatures. Allergy seasons are worsening, while the number of heart and respiratory health problems linked to the poor air quality and heat waves is increasing. This especially impacts certain parts of the population such as the elderly, children, and the poor.

Relatively small changes in the planet’s average temperature can lead to big changes in local and regional climate, creating risks to public health and safety, water resources, agriculture, infrastructure, and ecosystems. Climate change already has a serious impact on the world we live in today.

Every continent has warmed substantially since the 1950s. On average, there are more hot days, and the hot days are hotter. Heat waves have become longer and more frequent around the world over the past 50 years. This creates perfect conditions for extreme wildfire seasons around the globe.

Snow packs are melting earlier, leaving less water available during the heat of the summer. In some areas, this leads to reduced amounts of available freshwater, affecting major cities with droughts. Melting of sea ice and glaciers is also raising the global sea level.

Precipitation patterns are also changing. Air can hold more moisture as it warms. As a result, storms and floods are getting stronger and more frequent. This has a major impact on crops, some foods are becoming less nutritious. Increased atmospheric CO2 speeds up photosynthesis, the process that helps plants transform sunlight to food. While this makes plants grow faster, in doing so, they pack in more carbohydrates like glucose at the expense of other essential nutrients.

Many land and marine species have had to shift their geographic ranges in response to warmer temperatures. While some species may adapt to rapidly changing the land, freshwater, and marine habitats, others will suffer population declines, collapse and even extinctions.

Humans will suffer when some ecosystems no longer provide the services (food, coastal defense, clean water, etc.) we depend on. A major concern with the current episode of warming is that it is happening so rapidly that humans and nature might have insufficient time to adapt. Entire ecosystems, communities, and even countries are at great risk. Much of the human population lives in coastal areas that will be inundated by higher seas and larger storms, with property losses that will total billions of dollars in this century. Climate change is already prompting an increase in migration, with people being forced to leave their homes because of drought, flooding, and other climate-related disasters.

The Earth’s future climate will depend on whether we manage to slow or even reduce greenhouse gas emissions, but warming is likely to continue.

A rise in global temperatures increases the severity and likelihood of storms, floods, wildfires, droughts and heat waves. Climate change affects the weather by intensifying the water cycle. Water evaporates into the atmosphere from both land and sea and returns to Earth’s surface in the form of rain and snow. As the temperatures are rising, the rate of evaporation from our oceans is increasing. This creates perfect conditions for strong storms and hurricanes. Over the past 20 years, tropical storm activity in the Atlantic Ocean, Caribbean, and the Gulf of Mexico has increased in intensity, frequency, and duration. Increased rates of evaporation on land can lead to more rapid drying of soils and severe droughts. The extent of regions affected by droughts has also increased as precipitation over land has marginally decreased while evaporation has increased due to warmer conditions.

Global warming contributes to rising sea levels in two ways. First, hotter summers, warmer winters, and earlier springs are causing glaciers and ice sheets to gradually melt. The increased runoff from polar lands is causing sea levels to rise. Second, thermal expansion, the natural expansion of water as it heats up, is causing the ocean to take up more space, which also leads to rising sea levels.

Scientific calculations show decades of more ice losses than gains. On average, most of Earth’s mountain glaciers are continuing to melt. The Earth’s polar regions are especially vulnerable to global warming because temperatures in the Arctic and Antarctic are rising at twice the rate of the world average. Arctic and Antarctic sea ice volume and extent have been declining since record-keeping began in the late 1970s and prior.

Due to time lags in the climate system and the fact that CO2 stays in the atmosphere for hundreds or thousands of years, the climate will continue to warm until at least mid-century regardless of what we do today to reduce emissions. If we fail to make substantial cuts to greenhouse gas emissions, the Earth will keep warming for centuries to come.

The solutions to the climate crisis are numerous, but the most important goal is the urgent action to cut greenhouse gas emissions. This will require stepping up improvements in energy efficiency, reducing waste, slowing deforestation, and shifting to cleaner energy sources.

It requires global efforts such as international policies and agreements between countries, local efforts on the city- and regional level, but it is also a matter for personal action. There are many actions that individuals and business can take to reduce their carbon footprint and act on climate change. Simple actions such as using energy-efficient light bulbs and appliances, recycling and composting, purchasing green power, using public transit, and bicycling or walking instead of driving can make a difference by reducing your household’s carbon footprint.

Learn more

1. IPCC (2013). Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change.

2. IPCC (2007). Climate Change 2007: The Physical Science Basis. Frequently Asked Questions. FAQ 7.1. Intergovernmental Panel on Climate Change.

3. IPCC (2007). Climate Change 2007: The Physical Science Basis. Executive Summary. Intergovernmental Panel on Climate Change.

4.​ USGCRP (2016). The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment. Crimmins, A., J. Balbus, J.L. Gamble, C.B. Beard, J.E. Bell, D. Dodgen, R.J. Eisen, N. Fann, M.D. Hawkins, S.C. Herring, L. Jantarasami, D.M. Mills, S. Saha, M.C. Sarofim, J. Trtanj, and L. Ziska, Eds. U.S. Global Change Research Program.

5. USGCRP (2014).Climate Change Impacts in the United States: The Third National Climate Assessment. Melillo, Jerry M., Theres (T.C.) Richmond, and Gary W. Yohe, Eds., U.S. Global Change Research Program.

6. National Research Council (2011). Climate Stabilization Targets: Emissions, Concentrations, and Impacts over Decades to Millenia. National Academies Press.

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