On the evening weather report on October 15th, 1987, weatherman Michael Fish uttered the now infamous words "Earlier on today a woman rang the BBC to say she'd heard there was a hurricane on the way. Well, if you're watching: don't worry, there isn't." As it happens, an extra-tropical hurricane swept across the British Isles that night with wind speeds up to 100 mph leading to 18 deaths, $1.6 billion in damage and the loss of 15 million trees. And Michael Fish now is a weather legend for all the wrong reasons. Up to the latest part of the 20th century, British weather forecasters, in general, had a miserable reputation. If forecasters back then said rain, it was just as likely to be sunny and no one took much notice. However, since 1987, weather forecasting has become much more of a science than an art, with highly sophisticated models operated by powerful computers making weather predictions. You can rely on weather forecasts to be accurate in the most part. For example, extreme weather forecasts for events like hurricanes, tornadoes and snow storms are generally accurate as a result of these improved models. Some of these same models are now used to make projections about climate change. In this module, you will learn how models work and what predictions they are giving for the future.
We have already learned about very simple climate models that represent the whole earth in one box and slightly more advanced models that represent the Earth in a few latitudinal bands. As you might imagine, there is a whole spectrum of models, and at the far end in terms of complexity are GCMs — which can mean either General Circulation Model or Global Climate Model. There are GCMs that model just the atmosphere (AGCMs), just the oceans (OGCMs) and those that include both (AOGCMs). These models divide the Earth up into a big 3-D grid and then treat each little cube or cell similar to the way we treat reservoirs in STELLA models. The basic structure of a GCM can be seen in the figure below:
As you can see, the models include land, air, and ocean domains, and each of these domains is treated somewhat separately since different processes act within the various domains. The more cells in a model, the closer it can approximate the real Earth, but too many cells would require more computing power than is available. The history of these models is closely connected to the history of advances in computing power, and the current generation of high-end GCMs are among the most computationally-intensive programs in existence. Models are in a continuing state of development and evolution, so in the future, they will be more complex and realistic; with continued advances in computational power and reduction in the cost of runs, models are set to take on more ambitious tasks such as making very fine projections about an ever-expanding number of environmental variables. Combine them with robots and look out!
What’s so important about these models that people would devote their careers to building and refining programs that take days to run on the fastest computers? The power and utility of these models is that they can show us how climate changes on a regional scale, which is of utmost importance in planning for our future. In our future, we are probably going to be the most effective in dealing with climate change on the scale of regions like states and countries, so having a model that shows us what those regional changes are likely to be is a very important tool.
A key aspect of using models is understanding the uncertainty of their predictions. They are simulations, after all. As you will see, most of the results are cast in terms of ranges, for instance, the temperature predictions for 2100 under the different emission scenarios are given with significant bands of uncertainty. The reading for this module is the Summary of the Intergovernmental Panel on Climate Change for Policy Makers. When you read this, you will see how scientists convey the uncertainty of model predictions.
On completing this module, students are expected to be able to:
Below is an overview of your assignments for this module. The list is intended to prepare you for the module and help you to plan your time.
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Ever wonder how weather forecasts are so accurate? How predictions are made over days and weeks? How hurricanes and blizzards are forecasted? General circulation models (GCMs) are instrumental in weather forecasting. They are highly detailed grid-based simulations of weather that use atmospheric physics to predict events over hours, days, and even further into the future. These models are commonly used to predict climate change over years, decades, and centuries. GCMs have become more and more accurate as the physics of the atmosphere has become better understood. As computers have become more capable computationally, the models have become more accessible to the general public. Before, they required a mainframe computer. You can now run them on laptops! In this module, we explore how GCMs work.
In a highly simplified sense, the operation of GCMs can be thought of in a few basic steps.
This figure comes from a run of the NCAR model, called CCM (Community Climate Model) and represents the atmospheric pressure at sea level averaged over 10 years. Also shown is the pattern of winds that results from the combination of the forces due to the pressure differences (air flows from high to low pressure, driven by a Pressure Gradient Force and the Coriolis Force, which is related to the rotation of the Earth. The length of the arrows is proportional to the strength of the winds. Note that the model produces belts of pressure that are very similar to the observed pressure belts — low pressure near the equator, high pressure at 30N and 30S, low pressure at 50-60N and 50-60S, and high pressure again near the poles, patterns we learned about in Module 3.
This is obviously a very important question — if we are to rely on these models to guide our decisions about the future, we need to have some confidence that the models are good. The most important approach is to see if the model can simulate the known climate history. We set the model up to represent the state of the climate at some point in the past — say 1900 — and then we see how well the model can reproduce what actually happened.
As you can see in the figure below, the models are collectively quite good:
In this figure, the black line is the instrumental global average temperature (as an anomaly, which is a departure from the mean value from 1901 to 1950), the yellow lines represent the output from 58 model runs by 14 different models, and the red is the average of those 58 runs. The vertical gray lines are times of major volcanic eruptions, which are always followed by a few years of cooler temperatures.
Now, let’s look at how closely the models can simulate the spatial pattern of temperatures over the Earth. To begin with, we’ll look at January temperatures for the time between 2003 and 2005 — here is what we can reconstruct from observations (which are better in some places than others — we know the Sea Surface Temperature (SST) much better than the land temperature, since SST is very precisely measured by satellites).
Now, we look at the same time period from a model simulation.
You can see that in general, they are quite similar to each other, but we can gain a bit more insight into the relationship between the observations and the model by subtracting the model from the observations — the result is this:
Here, you can see that the model and the observations are generally quite close, within a couple of degrees of zero, where zero would be a perfect match. Areas that are yellow to orange are regions where the actual temperature is greater than the modeled temperature; blue areas are regions where the actual temperature is lower than the model. It would be a challenging task to figure out the cause of these differences, but at a very fundamental level, it is related to the fact that things like clouds are very important to the climate system, and the processes that actually form clouds occur on such a small scale that the models cannot resolve them. Cloud formation is one example of what the modelers call a sub-grid process, and modelers have to devise clever ways of getting around this. This is an area where refinements continue to occur, but for the time being, we see that the models do an impressive job, but not a perfect job. As you can see in the above results, models tend to underestimate the temperatures on land, so we should consider model results for the future to also underestimate the true temperatures.
If we average over longer time periods and also average many model results together, we get what climate modelers refer to as an ensemble mean, and these ensemble means do a remarkably good job of matching the observations, as is shown below.
In this figure, the observed annual mean temperatures for the time period 1961-1990 are represented by the black contour lines, labeled in °C (-56°C in Antarctica and about 24°C in equatorial Africa). The colors represent the model temperatures (from 14 models, for the same 1961-1990 time period) minus the observations; positive values mean the models estimate temperatures that are too high. On the whole, the models slightly underestimate the temperatures, and they have particular problems at very high latitudes and in areas that are topographically complex.
As mentioned above, one of the important aspects of GCMs is that they calculate the precipitation, and this provides another means of evaluating how good the models are. The figure below shows a comparison of the observed annual precipitation and the average of the 14 models used by the IPCC study.
As can be seen, the models, on average, do quite well at simulating the global pattern of precipitation.
AOGCMs calculate the circulation and temperature of the world’s oceans, and we compare their ability in that regard by comparing the model-generated temperatures averaged over 1957 to 1990 with the observations from that time period.
In this figure, we see the latitude-averaged observed ocean temperature from 1957 to 1990 in the contours, and the average model ocean temperatures minus the observed temperatures in colors. For most of the oceans, the models are ± 1°C from the observations.
In sum, it should be fairly clear that although these models are incredibly complicated, they do a fairly good job of reproducing the temporal and spatial characteristics of our climate, especially when we look at longer time averages. In other words, if we compare the model results for a given day with the actual observations of that day, the agreement is not very good; the agreement gets better on a monthly-averaged basis, and it gets even better on an annually-averaged basis. Today, models are in a constant state of improvement, with certain elements, such as the impact of clouds, needing a lot more understanding.
Before we look at the results of GCMs that run into the future, we have to understand a few things about the experimental setups that go into these models. In order to do these experiments, the modelers have to apply forcings just as we did with our simple climate model in Module 3, and the primary variable is CO2 (carbon emissions) added to the atmosphere through human activities such as burning fossil fuels, farming, making cement, etc.
The IPCC (Intergovernmental Panel on Climate Change) has developed a whole set of scenarios (about 40) that represent the possible carbon emissions history for the next 100 years. The carbon emission is used as a key variable in driving climate modeling for each scenario. These scenarios are known as Representative Concentration Pathways (RCPs) and each one is based on an emissions trajectory. The IPCC has focused on several individual RCPs that provide a range of emissions and climate scenarios. At the outset for simplicity in this module we do not use the RCP emissions terminology because it is not very intuitive. Instead, we use groups of RCPs known as families (also developed by the IPCC) defined by the severity of the cuts (or lack thereof!) and the amount of cooperation between countries. Each family or scenario is based on a bunch of assumptions about population growth, economic growth, and choices we might make regarding steps to minimize carbon emissions. You can read more about the whole set of emissions scenarios at Wikipedia: Special Report on Emissions Scenarios [4]. But for our purposes, we'll focus on 3 families representing very different emissions scenarios.
The first of these scenarios is called SRES A2, and it is commonly known as business-as-usual — in other words, it leads to a continuation of increased annual carbon emissions that follows the recent history. In effect, this scenario represents a somewhat divided world, one in which we just can't reach agreements on what to do about limiting emissions of CO2, so each country does what seems to be in its own best interests. This world is characterized by independently operating, self-reliant nations. This world also includes a continuously increasing population — it does not level off during this time period. Above all, in this world, decisions are based primarily on perceived economic interests, and the assumption is made that these interests do not include the development of alternative energy sources.
The second scenario, called SRES A1B is a bit more optimistic. This scenario envisions an integrated world characterized by rapid economic growth, a population that reaches 9 billion by 2050 and then declines gradually, and the rapid development of alternative energy sources that facilitate increased economic growth while limiting and eventually reducing carbon emissions. This scenario also assumes that there will be rapid development and sharing of technologies that help us reduce our energy consumption. One of the keys to this scenario is that countries are integrated — they act together and find ways to improve the conditions for everyone on Earth. At this point in time, A1B is an optimistic but realistic scenario; B1 would take a revolution in the way the world economies work. And A2 or "business as usual," well, we will show you this is not the road we want to travel!
The third scenario, called SRES B1, represents an even more integrated, more ecologically friendly world, but one in which there is still steady and strong economic growth. As in scenario SRES A1B, the population in this scenario peaks at 9 billion in 2050 and then declines. One way to think of this scenario is that it represents a rapid, strong, and global commitment to the reduction of carbon emissions — it represents the best we could possibly do, and yet it does not rely on miracle technologies. The only real miracle it requires is that we all quickly figure out how to think and act globally and not focus solely on our own national interests. Most projections ignore scenario B2, as the combination of regional and environmental strategies is highly unlikely.
In graphical form, here are the three emissions scenarios.
Each scenario shows emissions of carbon to the atmosphere (mainly from fossil fuel burning — FFB) in units of Gigatons of carbon per year (Gt C/yr; a GT is a billion tons!), so this is an annual rate. In terms of a STELLA model (which we will return to in the next Module), this represents a flow into the atmosphere. Roughly half of the carbon emitted will remain in the atmosphere and lead to a stronger greenhouse effect, which will, in turn, increase global temperature and change the climate in a variety of ways.
Next, we'll have a look at the main driver for emissions reduction, the Paris Climate Agreement and then frame the model scenarios in terms of their implications for climate change and climate policy.
The Paris Accord is a really big deal. This global climate agreement brokered by the United Nations and signed in December 2015 by some 195 countries went into effect in October 2016. A key goal of the accord is for all member countries to reduce greenhouse gas emissions to keep global temperature rise below 2o C of pre-industrial levels (measured in 1880). This is a threshold level above which most scientists agree that the impacts of climate change will be catastrophic, including flooding of large coastal cities by sea level rise, brutal heat waves, and droughts that could cause widespread starvation in developing countries. The agreement also lays the groundwork for countries to strive to reduce emissions to keep temperature increase below 1.5o C or 2o C. 1.5o C is considered to be the best case scenario given that we are currently at 1.1o C above 1880 levels, and it would need very drastic emissions cuts very quickly. Above 1.5o C low-lying island nations in the Pacific and Indian Oceans would likely end up underwater. The agreement also acknowledges that 2o C is a more likely warming target. As we've seen before, this is a significant amount of warming but much more favorable than the 3 or 4o C which would produce catastrophic climate effects.
There have been numerous previous climate agreements in the past, most notably the 1997 Kyoto Protocol that laid out stringent emissions targets for the countries that signed on. One of the reasons the Paris Accord was adopted by so many nations (only Syria and Nicaragua did not sign initially, but both now have) is because the impacts of climate change are becoming increasingly urgent. Although 127 countries signed on to Kyoto, the US did not.
The second reason the accord was so widely adopted is that the emission targets are voluntary, set by the individual countries based on what they believe is feasible. For example, the US’s goal was to reduce emissions by 26 percent by 2025. Once a country sets its target, it is required to abide by it and present supporting monitoring data. A country can change targets every five years. The downside of the flexibility is that many experts believe that with current targets, 1.5o C is impossible, 2o C is highly unlikely and 3o C is more realistic. So, countries will have to reduce emissions radically and rapidly to stave off the highly adverse impacts of climate change.
Two other key provisions of the Paris Accord is that richer developed countries have made a financial commitment to help poorer developing nations meet their targets, although there were no firm amounts in the agreement. President Obama pledged $2.5 billion to this fund while he was in office, but only $500 million was paid. Another key provision is that the agreement recognizes and addresses deforestation as a key element of emission reduction and for countries to use forest management strategies as part of their emissions goals. In fact at the climate summit in 2021, 100 countries including Brazil where deforestation has been particularly devastating, agreed to stop deforestation entirely by 2030, which would be a major step forward.
For the US, one of the key components of the emissions reduction strategies was the Clean Power Plan, introduced in 2014 under President Obama. The CPP set out to reduce emissions from electrical power generation by 32% based on the reduction of emissions from coal-fired power plants and the conversion to renewable sources of energy including wind, solar, and geothermal.
So, the strength of the Paris accord is that it is voluntary, highly transparent and collaborative. The downside is that it is voluntary! The general fear is that the agreement may not go far enough, fast enough.
However, that said, this is by far the most widely adopted agreement and the global scope is a massive accomplishment. So, it was a major disappointment on June 1st, 2017 when President Donald Trump signaled that the US would withdraw from the Paris Accord in 2020, the earliest time the US could pull out under the agreement guidelines. The fear is that if the US, the second largest producer of carbon, does not abide by its emissions goals, other countries might not as well. Trump's reasons were largely economic, that the conversion to renewable energy would be too expensive and hinder the bottom line of businesses, especially the fossil fuel industries. However, as we will see in this class, conversion to renewables has begun and has the potential to be a very large and highly profitable business. Moreover, cities, states, and businesses themselves, especially those in the northwest and on the west coast are already committed to reducing emissions, so at least part of the US will continue to collaborate with other countries to address the critical issue of climate change.
Trump's overall strategy involves defunding and repealing the Clean Power Plan, which happened in 2019. It was replaced by the Affordable Clean Energy Rule, which was invalidated by the courts in 2021.
Fortunately for climate, at least, President Biden reentered the Paris Agreement on 19 February 2021 and has set even more ambitious US emissions targets than those originally agreed upon. The Inflation Reduction Act passed in 2022 includes $369 billion to help individuals, communities, and industries switch to renewable energy. This will help the US meet its Paris targets, and possibly more importantly, the legislation shows important leadership on the international stage.
We will refer to the Paris Agreement in the remainder of the course. In this module, we discuss how models simulate the climate of the future. In closing, remember the 2oC number, it's going to come up over and over again.
In this section, we explore GCM predictions for future changes in temperature, precipitation, and surface water using three of the emission scenarios, A2, A1B, and B1. The models are driven primarily by estimates of CO2 input over the coming decades in each scenario.
In this section, we explore the predictions from GCMs regarding the temperature under different IPCC emissions scenarios described in the previous section. As part of the 2022 IPCC Assessment Report, all of the major GCM modeling groups around the world ran their models with the same Representative Concentration Pathways (RCPs) and emissions scenario families (A2, A1B, and B1) in order to provide the best estimate of what the future climate might look like under these scenarios. Each model is different, and so their results are also different. The figure below gives a sense of how much variability and similarity there is in these models. Remember that climate scientists believe that it is key that we maintain the warming below 2oC; above that level the consequences, including drought, heatwaves, melting of ice sheets, could be dire.
Each of the thin colored lines represents the output from a different GCM — here we see the average global temperature through time, starting in 1900 and going until 2099, using the SRES A2 scenario, which is the one we sometimes call "business as usual." There is obviously a big spread in the results, but they all have more or less the same general form and a similar range (the difference between the minimum and maximum temperatures). The thicker gray line is the mean from all these models, and we can see that by the end of the century, the mean rises by about 3.2 °C above the present temperature — this is roughly three times the warming we have experienced in the last century (about 1.1oC).
The differences in these curves reflect, among other things, different starting conditions, and if we force them to all have the same temperature today, the similarities are more apparent:
This view gives a sense of how much the models differ in their relative temperature changes over the next century. You can see that by the end of the century, the spread of temperatures is a bit less than 2°C, but most of the models fall within a half a degree from the mean temperature — the thick gray line. As we have discussed, this mean warming of 3.2 °C would be disastrous for the planet.
The lesson here is that the similarities in the models are far more important than their differences, and that they forecast a significant temperature rise by the end of the century as we essentially continue with our emissions of carbon into the atmosphere.
Next, let’s have a look at what the models say about the different emissions scenarios. Just to refresh your memories, here are those emissions scenarios again:
Recall that in these emissions scenarios, we talk about the annual rate of carbon emissions to the atmosphere and that carbon takes the form of CO2 in the atmosphere.
Here are the corresponding global temperature histories from the collection of models for these different scenarios:
As expected, the A2 scenario results in the highest temperature rise, followed by the A1B and the B1 scenarios. Remember that the A1B scenario represents a pretty optimistic view of how we will react to the challenge of climate change, but even in that case, the temperature rises by about 2.3 °C in the next century, which is above the Paris 2.0oC target. And even in the dramatic reduction in emissions envisioned in the B1 scenario, the temperature still rises by about 1.4°C — greater than what we’ve experienced in the last century. The lesson here is that we need to be prepared for continued climate change even if we take steps to limit carbon emissions into the atmosphere. And note that only B1 keeps us below the catastrophic 2.0oC threshold discussed earlier.
Next, we turn to the really interesting aspect of the GCM results, which are the spatial patterns of climate change. Why is this so interesting and important? The reason is that what really matters to us is how the climate changes in key areas -- areas that will affect sea level through melting of glacial ice, areas where people are concentrated, and areas where we produce food to feed ourselves. Remember that the climate of the Earth is highly variable, and if we talk about a global temperature rise of 3.2 °C, we have to remember that the temperature will rise more than that in some places (such as the polar regions) and less than that in the tropics.
We begin with a look at the climate of the future as predicted by the NCAR (National Center for Atmospheric Research in Boulder, Colorado) model — this model seems to often fall close to the mean of all the other models.
Here, we see 4 views of the surface temperature anomaly relative to the 1960-1990 mean. The upper 2 panels represent the mean temperature anomaly for the 20-year period from 2046 to 2065 in the month of July on the left and January on the right. In the lower 2 panels, we see similar views for the 20-year period from 2080 to 2099. The color scale below is for all of the panels and shows the anomaly in °K, but you can also think of this as °C.
Let’s start with the mid-century forecast. It calls for moderate warming in the range of 2 °C relative to the 1960-1990 mean. For both months, the warming is slightly higher on land than in the oceans, and there are a couple of spots of cooling: at the southern edge of the Sahara, one in the Southern Ocean around Antarctica, and one in the North Atlantic. The striking feature of this model result is the big change in the high latitudes of the Northern Hemisphere in January, where a large region will experience much warmer winter temperatures — up to 10° C warmer. The changes are even more dramatic for the end of the century, with the northern winters warming by up to 20° C above the 1960-1990 mean! This clearly spells the end of polar ice, which already is at its lowest extent ever. Notice also that much of Canada and Siberia warm by up to 10° C; this has important implications for the reduction in permafrost, which will increase the flow of carbon into the atmosphere via positive feedback (more on this in the next module).
The pattern of warm winters is important for a number of reasons. For one thing, it means that there will be less growth of ice in glaciers during the time of the year that they accumulate ice; thus they will shrink faster, and sea level will rise at a higher rate. Another unexpected result of warmer winters is increased problems from some insect pests whose populations normally are greatly reduced by cold winters, and when it does not get cold enough in the winter, they expand their range and cause greater damage. This is already happening in the western US with the pine bark beetle, whose population has exploded in recent years, leading to the decimation of large forested areas. These dead pine trees are then fuel for large forest fires whose scale exceeds fires in the historical record.
On the other hand, warmer winters lead to lower energy demands for heating, but this is offset by the greater energy demands for cooling during the summer months (the season where cooling would be required will also increase).
For comparison, we now compare the end-of-century forecast for the other 2 scenarios, A2, which is our business as usual case, and B1, which is our optimistic case with the A1B forecast.
First, we have the A2 scenario, which leads to the highest warming:
For July, this scenario leads to warming of the continents that is between 5 and 10° C — very little of the US would escape warming in excess of about 6° C; the same is true for much of Europe. Winter shows an even more dramatic warming, with vast regions warming by more than 20 °C. A2 would result in a major increase in the number of days over 90o F in places such as Atlanta and Austin, literally half of the days of the year would exceed that level of discomfort. And polar ice would melt much faster than in A1B or B1.
Now, for the other extreme, the B1 scenario at the end of the century:
As expected, the warming is far less dramatic, but still shows an impressive warming at the high latitudes of the Northern Hemisphere in the winter months. But, for most of the land areas, where people are concentrated and where we grow a lot of our food, the climate changes generally do not exceed a warming of 2°C.
These comparisons demonstrate the importance of reducing emissions to scenario B1 levels.
Next, we turn to precipitation, and we will be looking at anomaly maps, showing the difference between the model’s precipitation rate (i.e., mm/month or mm/day) and the average precipitation rate for 1960-1990. It will help us to begin with a glimpse of actual typical precipitation rates for the two months of interest here — January and July. Below, we see this pair of maps:
Here, red is used to show high rainfall, and blue is used to show low rainfall. The precipitation rates here are shown in terms of millimeters of rain per day (the high value here of 15 mm/day is about 0.6 inches/day). In the eastern US, for instance, the July rainfall is about 1 mm/day.
Looking into the future, we will again utilize the results from the NCAR model, looking at 20-yr. means from the climate model results to get a smoothed out version of the results. We focus here on the SRES A1B scenario, looking at the months of July and January.
The units here are a bit odd — kilograms per meter squared per second — but it is still a rate, and if we do a conversion, we find that 0.0001 of these units equals 25 centimeters per month, or a bit less than one centimeter per day, which is 10 millimeters per day. Have a look at the map for July 2040 to 2069. In the eastern US, the precipitation anomaly is about 2e-5 kg/m2s. At first, it is difficult to get a sense of whether this is important. This anomaly translates to about 45 mm/month, or about 1.5 mm/day. From the July 2001-December 2019 map above, we see that the typical rate for July in this region is on the order of about 4-5 mm/day, so an increase of 1.5 mm/day is about a 30% increase — fairly significant.
Interestingly, there is not much of a change in the anomaly for this region (eastern US) as we move to the end-of-the-century map in the lower left panel above. And in both cases, the western US is drier by a bit. If we look at the January maps, we see a bigger change between the two time periods, getting drier by the 2080-2099 period. For January, it is important to note that in the Western US, the precipitation is decreased; this could mean problems for the water supply out west, where the winter snows, as they melt in the summer, represent a major part of the water budget.
What about the other scenarios? Below, we see the precipitation anomaly maps for the A2 scenario — the one that leads to a hotter climate — for the 2080-2099 time period.
Compared to the A1B scenario for the same time period, we see a generally drier picture for the US, though the differences are actually quite small. Note that in this scenario, as in others, the tropics get wetter.
For scenario SRES B1, the one where the temperature at the end of the century is only slightly higher than the present, the precipitation changes are generally quite small, as can be seen in the map below:
Here, the very light colors indicate slight increases and decreases in the precipitation rate for July — the same picture holds for January as well.
In summary, the precipitation results indicate that in general, there are very complicated patterns of precipitation change, and for much of the globe, they are quite minimal. The model predicts that there will be wetter areas and drier areas, and what really matters is how these changes correlate with the regions where people live and grow their food. Under the A2 scenario (business-as-usual), we should expect a generally drier western and south-central US and a generally wetter northeastern U.S. We will revisit this issue in Module 8.
As was mentioned in the previous section on precipitation predictions from GCMs, there are some important implications for water supplies. In this section, we will take a look at how the model results might impact surface water in the future.
Surface water is of great importance since it is the primary source of water for agriculture. It is estimated that 69% of worldwide water use is for irrigation, and of this, about 62% comes from surface water which includes streams, lakes, and reservoirs.
When rain falls on the surface, most of it is absorbed by the soil, and then from there, it slowly migrates to streams, and from there into lakes and reservoirs, but it ultimately leaves a region, through stream flow and evaporation, to return to the oceans or the atmosphere — this is just a part of the large global water cycle. The total amount of water flowing in the streams of a region provides a useful measure of how much water is available for agriculture. We'll discuss this in depth in Module 9.
It takes around 3,000 liters of water to produce enough food to satisfy one person's daily dietary needs. This is a considerable amount when compared to that required for drinking, which is just 2-5 liters. To produce food for over 7 billion people who inhabit the planet today requires the water that would fill a canal ten meters deep, 100 meters wide and 7.1 million kilometers long – that's enough to circle the globe 180 times!
As we have seen, a GCM will predict the amount of rainfall over the surface of the Earth, and if we combine that with a model of the topography of the land areas (which is included in the GCMs), we can figure out how much water will flow as surface water through different regions. This has been done by taking the average precipitation from 20 GCMs operating under the SRES A1B scenario and then calculating how that surface water flow compares to the long-term average from 1900 to 1970. The resulting data provide us with a very good idea of what to expect in the future if we follow the A1B scenario.
The results of the surface water predictions can be seen here, but we will focus in on 3 snapshots from this history in a series of maps. We begin with a view of the predictions for the year 2020.
This image is a 30-year average, centered on the year 2020. The blue areas will see an increase in streamflow, and the red areas will see a decrease. The map includes contour lines that separate the values according to the labeled tick marks on the map scale. As we might expect, the changes are relatively small at this point, but the Mediterranean region has noticeably less streamflow.
As we move forward in time, to 2050, the changes become more dramatic.
And as we continue into the future, the picture in 2080 looks like this:
By the year 2080, we see that there are some fairly stark differences in streamflow. A large swath around the Mediterranean, including much of Europe, North Africa, and the Middle East all will have significant reductions in streamflow, which will add stress to an agricultural system that is already operating at close to its limit. Consider also that the world by this time will certainly have a minimum of 10 billion people. Much of the Southwestern US (including California) and Central America will also experience a reduction in streamflow. This is clearly bad news for a place like California, which is already going to great lengths to bring surface water to its cities and major agricultural production areas via a system of canals. And for Central America, continuing drought will lead to even more mass migration to the US southern border. We will discuss this in much more detail in Module 9.
On the other hand, consider where most of the Earth's population is — China and India. Both of these regions, according to the model results, should experience an increase in surface water availability, which is good news.
Another big trend is that the high latitudes of the Northern Hemisphere experience a very large increase in surface runoff. This could be important if population patterns shift northward in a warming world.
We now consider how these changes add up over the whole globe — is surface runoff increasing or decreasing as we go into the future? The 3 maps shown above have been averaged along lines of latitude to give a simpler sense of the change, and then these latitudinal averages are summed and weighted according to the different areas each latitudinal band represents to give a global sum.
As can be seen, the tropics and the high latitude regions tend to get wetter through time, while the mid-latitudes tend to become drier, and on a global scale, there is slightly more surface water runoff as we move into the future — though a 3.3% change is not too large. Nevertheless, remember the map pattern of the change — this is the more important aspect of the model data.
So, in summary, as with precipitation, the future seems to hold a mixture of more and less surface water runoff, and this will have some important implications for where we will produce our food in the future and which places may be better suited for human habitation in the future. We will discuss the implications of the projections for regional drought in places such as the south-central US and Australia in more detail in Modules 9.
In this module, you should have grasped the following concepts:
You should have read the contents of this module carefully, completed and submitted any labs, the Yellowdig Entry and Reply, and taken the Module Quiz. If you have not done so already, please do so before moving on to the next module. Incomplete assignments will negatively impact your final grade.
Links
[1] https://creativecommons.org/licenses/by-nc-sa/4.0/
[2] https://www.ipcc.ch/
[3] https://www.google.com/earth/
[4] https://en.wikipedia.org/wiki/Special_Report_on_Emissions_Scenarios
[5] https://commons.wikimedia.org/wiki/User:L.tak
[6] https://creativecommons.org/licenses/by-sa/4.0
[7] https://gpm.nasa.gov/data/imerg/precipitation-climatology#monthlyprecipitationclimatology
[8] https://www.nasa.gov/