Overview Of Future Climate Projections
Mburucuyá National Park is a protected subtropical wetland about 17.7 HA in size and is located in the north-western part of the Corrientes province in Argentina. The land was donated by Troels Pedersen and the park was created in 2002. It houses important plant and animal species including the Quebracho Blanco tree, Neotropical River Otter, Manned Wolf and Marsh Deer all of which are endangered. It contains forests such as hydrophytic and chaco, grasslands, Mesopotamian-type wetlands and palm savannas. It has a hot and humid climate.
The hottest months are December to February (averaging about 78.3, 80.2 and 79.3 degrees Fahrenheit respectively) and the coldest months are June to August (averaging about 60.4, 59.4 and 62.1 degrees Fahrenheit respectively). The average amount of precipitation in a year is about 1300mm, averaging about 5.3 wet days in a month with 4.11 inches of rain a month. Winter and fall are the wettest and driest seasons.
Predicted average temperature anomalies in Mburucuyá National Park, Argentina for each month from 2041-2070 for 4 different climate prediction models (CGCM3T63, BCM2.0, MICROC3 2 (hires), HadCM3) were plotted with standard errors of mean (0= no change in temperature from baseline; baseline years: 1986-2015). A positive change indicates climate warming whereas negative change indicates climate cooling. As expected, models that predict weather and climate over a long period of time are going to have different projections as a result of physical attributes or anthropogenic factors (i.e. greenhouse gases). In this graph, we can see that prediction 2 and prediction 3 have the lowest and highest overall temperature anomalies respectively which could be due to factors that each model considers which are not considered by the other models.
We can also see that prediction 4 has more variability (i.e. spikes) in temperature from month to month while prediction 1 doesn’t have as much. However, all the trends are indicating that the climate is changing and is getting warmer overall, which can be attributed to greenhouse gases. Models might consider different factors (i.e. albedo, cloud cover, pollutants) which may have a stronger or weaker effect on climate which results in differences in their projections. As well, there are so many factors that influence climate, we cannot measure everything in 1 model. As a result, we use multiple models which differ in their resolution and thus their complexity.
A climate warming would result in positive temperature anomalies and a climate cooling would result in negative temperature anomalies. The graph shows overall varying trends between the 2 scenarios. The temperature anomalies are higher in SR A1B than SR B1, which could be due to the fact that SR A1B considers things that make the temperature more sensitive to changes than SR B1. Also, the lines do not tend to overlap very much either which is possible due to the varying scenarios, which would change the circumstances and thus alter the projections.
Scenarios can map out possible situations in the future (i.e. change in technology, population changes) and how these things are going to lead to different greenhouse gas concentrations and thus variances in temperature. Scenarios are also reliant on how we (humans) respond to changes in climate and can dictate where our future efforts should be focused on. The different scenarios are going to impact climate differently and these things might increase or decrease climate sensitivity which would result in higher or lower temperature anomalies.
