Weather projections in the Cauca river valley, June-July 2020

In science, the exercise of prediction is key to confronting hypotheses (Wassermann, 2017^[1]). In this sense, when a prediction is not fulfilled, the hypothesis is refuted without appeal.

In climatology, predictions (in the spatial context) and projections (in the temporal context) are the result of an exercise in which the physical processes and laws that determine the state of the troposphere, climate factors and interaction are collated. among them.

Therefore, starting from the hypothesis that air behaves like an ideal gas, it can be predicted that the air temperature in the zones at 1400 masl is significantly lower than the air temperature in zones at 1000 masl.

However, when you want to project the future climate, you must assume changes in its factors. These changes determine variations in the values ​​of the variables that characterize the elements of the climate of a region. The most common example of this type of projection is that of air temperature values ​​due to climate change. To do it, models are used in which changes in CO emissions are assumed₂

In Cenicaña, short-term climate projections (months) are made on a monthly scale and take into account latitude, the location of the Cauca river valley in the middle of the Andes, and the effect of the Caribbean and the Pacific:

• Due to the effect of latitude, the climate of the valley is subject to two annual maximums of solar radiation and precipitation.
• Due to the effect of the mountain ranges, the rains are mainly nocturnal and the air flow on the surface responds to the appearance of the valley.
• The proximity to the oceans determines entrances of humid air at some times of the year. Likewise, changes in sea surface temperature affect atmospheric pressure patterns around the globe, determining variations in the region's climate.

Based on the proposed hypotheses, it is expected that, in general in the Cauca River Valley, the climate during the two-month period June-July will follow the historical average pattern.