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DICCE-G Extended Help: Climate Trends and Global Warming

An introductory discussion of why examining trends in climate data is necessary for the evaluation of climate change (aka "global warming")

                To investigate the effects of global warming, (also known as climate change), on a specific region of the Earth we need accurate data sets acquired over a sufficiently long period of time to detect a climate trend.   Some of the data sets in the DICCE-G archive have shorter time ranges than necessary to observe a real climate change trend, yet they can still be used to create a multi-dimensional view of a region’s climate. This type of study establishes the "normal" regional climate, which consists of typical patterns of weather over a typical year. It is often expressed in terms of average conditions such as the average air temperature or average amount of precipitation.. Only if the average weather conditions change in a consistent way over several decades, can we start to claim that the region's climate is changing. 
 
                Many factors, such as temperature and precipitation, affect the weather. The key activity in observing a climate trend in a particular region is to examine a variety of data from as far back as possible to see if the normal seasonal or annual variances in the weather are changing.   Day-to-day, month-to-month, or year-to-year weather is so changeable that each  type of weather data has to be averaged and observed over many years to see if there is such a trend. Climatologists call the normal range of variability in the climate system the “noise” and consistent long-term shifts in the system the "signal".  
 
                One of the main ways to differentiate the signal from the noise is to create and analyze a time-series of data. A time-series is a graph showing the values of the data plotted against time.    DICCE-G makes it easy to produce time-series of graphs for any region of the Earth. When you look at your time-series graph, you may not see what you would intuitively expect.. The following are examples of both expected (intuitive) and unexpected (counter-intuitive) trends in regional climate data.  
 
                One of the first data products to examine when considering regional climate change is temperature.   A time-series of temperature data in a warming world would usually be expected to show a trend of higher  temperatures over time.   However, such a trend may not be always obvious . In many places, summer temperatures have not shown much of a change over recent decades. Yet, in some of the same places, winter temperatures (such as average daily temperatures, average low temperatures, and average high temperatures) have shown a trend of increasingly high values.. Furthermore, it is possible (but unusual) to find regions with cooling trends over recent decades.   Regional cooling trends may be caused by shifts in weather-related processes such as winds or humidity, which may change due to the additional energy in the Earth’s climate system that is present due to the overall warming trend taking place at the global level.
 
                Recently, despite the many factors that are probably contributing to global warming, Earth’s average surface temperatures have not increased as fast as some scientists expected.   Research has indicated that several factors have probably held back the expected rise of Earth’s temperatures over the last 10 to 15 years, including a slight effect from reduced sunspot activity and a more prominent effect from increased atmospheric pollution being generated in China due as a result of its rapid industrialization.  Absorption of heat by deep ocean waters has also been examined.  Hence, many factors have to be considered when examining a time-series for trends.
 
                Another common data product that can be examined is precipitation. As the Earth gets warmer rainfall patterns are expected to change but not always in the same direction.. Some places may get more rainfall and others may get less and both trends could be due to global warming    Increased evaporation from the oceans puts more water vapor into the atmosphere, which can transform into rain and result in heavier precipitation. Yet, over land, particularly in the interiors of continents, warmer overall temperatures can dry out the land surface, reducing the soil moisture that under slightly cooler conditions would have evaporated into the atmosphere and condensed to fall as rain.
 
                Snowfall patterns can produce both intuitive and counter-intuitive trends as well. In general, if regional climate is getting warmer, there will be less snow falling and accumulating on the ground over time (in areas where snowfall occurs).   This is due to atmospheric temperatures being warmer so that more precipitation falls as rain than as snow. It is also due to land (or near-surface air) temperature being warmer, causing falling snow to melt faster. Yet, a counter-intuitive trend has been observed in land areas near the North American Great Lakes. These areas receive “lake-effect” snow, which is caused by the condensing and subsequent freezing of more water vapor evaporated from the lake surfaces. Recently, the numbers of days in which these areas have been experiencing their coldest winter temperatures have been decreasing, causing the lake surfaces to become iced-over for fewer days. When the lake surfaces become icy, the water in them cannot evaporate. Now that they are icing over for fewer days, there are more days in which evaporation can occur. The water vapor that forms from this evaporation moves over the land and condenses into precipitation. Instead of falling as rain however, the precipitation falls as snow because the winter climate of the region is characterized by the passage of cold air masses from the north. Therefore, these areas have been getting more snow even though their winter temperatures have been increasing.
 
                In closing, it can be challenging to examine time-series of Earth data to detect climate trends, and to properly relate observable trends to their causes.   It is particularly challenging if the trends are not apparent, or if they are counter-intuitive. These examples provide good teaching opportunities to show the relationships of the many different factors in Earth’s weather and climate systems. Generally speaking, it is important to look for trends in more than one type of data, and consider how the data may be related.  Furthermore, you can look at correlations between changes in one type of data and changes in another.  Examples may be euphotic depth and cloud fraction, or sea surface temperature and land surface temperature, or net longwave radiation and net shortwave radiation. Think about what types of data may show trends that could be related to global warming over a period of years. Examples may be the different temperature data sets, or or one of the precipitation or snow data sets. Also, since trends may vary from one region to another, it can help to give students a more global view if you compare several different regions, such as one that is near the interior of a continent and one that is on the coast, or in regions at different latitudes.

         Regional maps of data can also be used to examine patterns of climate change related to trends.   A map of a climate-related data product represents a snapshot in time;   if these data have a trend over time, then maps of the data plotted for sufficiently-separated intervals of time will display spatial patterns of change related to the trend.   Such trends are most visible when the largest changes over time can be compared.   An example of such a comparison is found in the article "Climate change makes waking up to smell the coffee more expensive", where changing temperatures in the mountains of Costa Rica (a prime coffee-growing country) can be observed.  In the article, temperature maps for the cool year of 1980 and the warm year of 2010 are shown, and distinct differences are evident.   Such differences would not be as evident when there is a smaller temperature difference between the time periods that are being compared.   Thus, both data time-series and data maps should be used together to evaluate climate change patterns occurring in a specific region over time.

 

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Last updated: Sep 23, 2011 12:53 PM ET
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