How do weather models work

This particular equation is used to solve for vertical motions in the atmosphere. The history of using mathematical models to forecast weather and water can be traced back to the early 20th century but only became useful with the advent of computers. Once computers were available however, numerical weather prediction evolved quickly. As computing power grew, forecasts were able to be computed more regularly but their skill levels were far below the manual techniques at the time.

Then, into thes, increased computing power combined with an influx of new observations, including satellite data, kickstarted the field of weather modelling as they began to compute more data with more speed and skill. In modern times, continuing advancements have allowed for more complex models to be run at a finer scale over the entire globe, further increasing their predictive skill.

In what months does it rain the most? Will it rain more often next year? What about ten years from now? Or 50 years from now? For short-term climate predictions over periods of a few weeks to a few years, such as seasonal and El Nino forecasts, the model is started from our best guess at the current state of the ocean, ice, land and atmosphere. The ocean, especially, reacts rather slowly and allows such forecasts to have skill much longer than a weather forecast.

For climate projections over periods of decades to centuries and longer, the model is started from a statistical mean state and spontaneously develops its own weather and climate.

Weather Models. Model Complexity. Climate vs weather and the components of the climate system. In other words, if a model is not producing enough rainfall in Europe, it might be for reasons other than the North Atlantic, explains Maraun. For example, it might be because the modelled storm tracks are sending rainstorms to the wrong region.

This reinforces that point that scientists need to be careful not to apply bias correction without understanding the underlying reason for the bias, concludes Maraun:. One of the most important outputs of climate models is the projection of global surface temperatures.

Scientists can then assess the accuracy of temperature projections by looking at how individual climate models and the average of all models compare to observed warming. Historical temperature changes since the late s are driven by a number of factors , including increasing atmospheric greenhouse gas concentrations, aerosols, changes in solar activity, volcanic eruptions, and changes in land use.

Natural variability also plays a role over shorter timescales. If models do a good job of capturing the climate response in the past, researchers can be more confident that they will accurately respond to changes in the same factors in the future.

Carbon Brief has explored how climate models compare to observations in more detail in a recent analysis piece , looking at how surface temperature projections in climate models since the s have matched up to reality. Model estimates of atmospheric temperatures run a bit warmer than observations, while for ocean heat content models match our best estimate of observed changes quite well.

Comparing models and observations can be a somewhat tricky exercise. The most often used values from climate models are for the temperature of the air just above the surface.

However, observed temperature records are a combination of the temperature of the air just above the surface, over land, and the temperature of the surface waters of the ocean.

Comparing global air temperatures from the models to a combination of air temperatures and sea surface temperatures in the observations can create problems. These blended fields from models show slightly less warming than global surface air temperatures, as the air over the ocean warms faster than sea surface temperatures in recent years. The blended fields from models generally match the warming seen in observations fairly well, while the air temperatures from the models show a bit more warming as they include the temperature of the air over the ocean rather than of the sea surface itself.

The longer period of model projections from through is shown in the figure below. Projections of the climate from the mids onwards agree fairly well with observations. There are a few periods, such as the early s, where the Earth was a bit cooler than models projected, or the s, where observations were a bit warmer. For the period since , when observations have been a bit lower than model projections, a recent Nature paper explores the reasons why this happened.

The researchers find that some of the difference is resolved by using blended fields from models. They suggest that the remainder of the divergence can be accounted for by a combination of short-term natural variability mainly in the Pacific Ocean , small volcanoes and lower-than-expected solar output that was not included in models in their post projections.

Global average surface temperature is only one of many variables included in climate models, and models can be evaluated against many other climate metrics. Model projections have been checked against temperature observations on the surface , oceans and atmosphere , to historical rain and snow data, to hurricane formation , sea ice extent and many other climate variables.

Models generally do a good job in matching observations globally, though some variables, such as precipitation , are harder to get right on a regional level. The accuracy of projections made by models is also dependent on the quality of the forecasts that go into them.

For example, scientists do not know if greenhouse gas emissions will fall, and so make estimates based on different scenarios of future socio-economic development. This adds another layer of uncertainty to climate projections. One example is that ice sheets could destabilise as they melt, accelerating expected global sea level rise.

Yet, despite models becoming increasingly complex and sophisticated, there are still aspects of the climate system that they struggle to capture as well as scientists would like. Clouds are a constant thorn in the side of climate scientists.

They cover around two-thirds of the Earth at any one time, yet individual clouds can form and disappear within minutes; they can both warm and cool the planet, depending on the type of cloud and the time of day; and scientists have no records of what clouds were like in the distant past , making it harder to ascertain if and how they have changed.

A particular aspect of the difficulties in modelling clouds comes down to convection. On hot days, the air warms quickly, which drives convection.

This can bring intense, short-duration rainfall, often accompanied by thunder and lightning. Convectional rainfall can occur on short timescales and in very specific areas. Global climate models, therefore, have a resolution that is too coarse to capture these rainfall events.

Thus, we would have low confidence in future projections of hourly rainfall or convective extremes from GCMs or coarse resolution RCMs. Carbon Brief will be publishing an article later this week exploring climate model projections of precipitation. To help overcome this issue, scientists have been developing very high resolution climate models. These have grid cells that are a few kilometres wide, rather than tens of kilometres. However, the tradeoff of having greater detail is that the models cannot yet cover the whole globe.

But expanding these convection-permitting models to the global scale is still some way away, notes Kendon:. It governs the annual rainfall patterns of much of the tropics, making it a hugely important feature of the climate for billions of people. Source: Creative Commons. The ITCZ wanders north and south across the tropics each year, roughly tracking the position of the sun through the seasons. Global climate models do recreate the ITCZ in their simulations — which emerges as a result of the interaction between the individual physical processes coded in the model.

However, as a Journal of Climate paper by scientists at Caltech in the US explains, there are some areas where climate models struggle to represent the position of the ITCZ correctly:. However, for a brief period in spring, it splits into two ITCZs straddling the equator. Most GCMs show some degree of the double ITCZ issue , which causes them to simulate too much rainfall over much of the southern hemisphere tropics and sometimes insufficient rainfall over the equatorial Pacific. The main implication of this is that modellers have lower confidence in projections for how the ITCZ could change as the climate warms.

But there are knock-on impacts as well, Xiang tells Carbon Brief:. The existence of [the] double ITCZ problem may lead to an underestimation of this weakened trade wind. Trade winds are near-constant easterly winds that circle the Earth either side of the equator. In addition, a study in Geophysical Research Letters suggests that because the double ITCZ affects cloud and water vapour feedbacks in models, it therefore plays a role in the climate sensitivity. Climate sensitivity: The amount of warming we can expect when carbon dioxide in the atmosphere reaches double what it was before the industrial revolution.

If models underestimate ECS, the climate will warm more in response to human-caused emissions than their current projections would suggest. There are likely to be a number of contributing factors, Xiang says, including the way convection is parameterised in models. As for the question of when scientists might solve this issue, Xiang says it is a tough one to answer:.

However, we have made significant progress with the improved understanding of model physics, increased model resolution, and more reliable observations. Finally, another common issue in climate models is to do with the position of jet streams in the climate models. Jet streams are meandering rivers of high-speed winds flowing high up in the atmosphere.

They can funnel weather systems west to east across the Earth. As with the ITCZ, climate models recreate jet streams as a result of the fundamental physical equations contained in their code.

Because models underestimate this, the jet is often too far equatorward on average. Storms are often too sluggish in models, says Woollings, and they do not get strong enough and they peter out too quickly. There are ways to improve this, says Woollings, but some are more straightforward than others. In general, increasing the resolution of the model can help, Woollings says:.

More complicated things also happen; if we can get better, more active storms in the model, that can have a knock-on effect on the jet stream, which is partly driven by the storms.

The process of developing a climate model is a long-term task, which does not end once a model has been published. Most modelling centres will be updating and improving their models on a continuous cycle, with a development process where scientists spend a few years building the next version of their models. Credit: Met Office. Once ready, the new model version incorporating all the improvements can be released, says Dr Chris Jones from the Met Office Hadley Centre:.

We do the same with our climate models. At the beginning of each cycle, the climate being reproduced by the model is compared to a range of observations to identify the biggest issues, explains Dr Tim Woollings.

How this is done varies from case to case, says Woollings, but will generally end up with some new improved code:. This may well be motivated by new research, or the experience of others [modelling centres]. In these cases, Process A will generally be fixed, even if it makes the model worse in the short term. Then attention turns to fixing Process B. At the end of the day, the model represents the physics of both processes better and we have a better model overall.

They monitor the biases in their area as the model develops, and test new methods to reduce these. These groups meet regularly to discuss their area, and often contain members from the academic community as well as Met Office scientists. The improvements that each group are working on are then brought together into the new model. Once complete, the model can start to be run in earnest, says Jones:. One of the main limitations of global climate models is that the grid cells they are made up of are typically around km in longitude and latitude in the mid-latitudes.

When you consider that the UK, for example, is only a little over km wide, that means it is represented in a GCM by a handful of grid boxes. Such a coarse resolution means the GCMs miss the geographical features that characterise a particular location. In essence, this means taking information provided by a GCM or coarse-scale observations and applying it to specific place or region. Tobago Cays and Mayreau Island, St.

Vincent and The Grenadines. For small island states, this process allows scientists to get useful data for specific islands, or even areas within islands, explains Taylor:.

There are two main categories for methods of downscaling. This is essentially running models that are similar to GCMs, but for specific regions. HadRM3 uses grid cells of 25km by 25km, thus dividing the UK up into squares. The map below shows how the greater detail that the 25km grid six maps to the right affords than the 50km grid two maps on far left ,. RCMs such as HadRM3 can add a better — though still limited — representation of local factors , such as the influence of lakes, mountain ranges and a sea breeze.

Darker red shading shows larger amounts of warming. Despite RCMs being limited to a specific area, they still need to factor in the wider climate that influences it. Scientists do this by feeding in information from GCMs or observations. Taylor explains how this applies to his research in the Caribbean:.

This involves using observed data to establish a statistical relationship between the global and local climate. Using this relationship, scientists then derive local changes based on the large scale projections coming from GCMs or observations.

One example of statistical downscaling is a weather generator. It uses a combination of observed local weather data and projections of future climate to give an indication of what future weather conditions could be like on short timescales. Weather generators can also produce timeseries of the weather in the current climate.

It can be used for planning purposes — for example, in a flood risk assessment to simulate whether existing flood defences will cope with likely future levels of heavy rainfall. In general, these statistical models can be run quickly, allowing scientists to carry out many simulations in the time it takes to complete a single GCM run. It is worth noting that downscaled information still depends heavily on the quality of the information that it is based on, such as the observed data or the GCM data feeding in.

Downscaling only provides more location-specific data, it does not make up for any uncertainties that stem from the data it relies on. Statistical downscaling, in particular, is reliant on the observed data used to derive the statistical relationship. Downscaling also assumes that relationships in the current climate will still hold true in a warmer world, notes Mitchell. For that reason, statistical downscaling is poorly constrained for future climate projections. Dynamical downscaling is more robust, says Mitchell, though only if an RCM captures the relevant processes well and the data driving them is reliable:.

However, if done well, dynamical downscaling can be useful for localised understanding of weather and climate, but it requires a tremendous amount of model validation and in some cases model development to represent processes that can be captured at the new finer scales. Updated on 15 January to clarify that the one million dollar prize for solving the N-S equations are for proving the existence of a solution in all circumstances, and that the grid boxes only converge towards the poles when the grid is based on latitude and longitude.

Carbon Brief would like to thank all the scientists who helped with the preparation of this article. Tomorrow: An interactive timeline of the key developments in climate modelling over the past century. Get a Daily or Weekly round-up of all the important articles and papers selected by Carbon Brief by email.