## 3 Results  
### Climate Variables and Energy Fluxes  
In carbon cycle characterization work, a complete and in-depth description of local atmospheric patterns is required, which are usually the main controls on site productivity variability. The study site showed marked seasonality in terms of rainfall and air temperature (Figure 2), with more intense precipitation events (rainy days) during the region's rainy season (December to May) and less intense events during the dry season (June to November). The large number of intense rainfall events causes the highest density of daily air temperature averages to be below 25 \({}^{\circ}\)C in the months of February and March, and the greatest variability of air temperature occurs at the end of the rainy season, with temperatures between 18 and 27.5 \({}^{\circ}\)C, in the month of May. The wind circulation patterns are shown in Figure 3. Throughout the year, the highest frequency of wind direction is from the NE-E quadrants, with more than 40% of the local  
Figure 2: Data density distribution of monthly air temperature (\({}^{\circ}\)C) by precipitation intensity (mm/day) in Caxiauña-PA.  
wind coming from the NE direction in October. The highest average speed occurred in December (1.9 m/s), and the lowest average speed occurred in April (1.4 m/s). Wind speed tended to increase (highest averages and highest occurrences in the 2-4 and 4-6 m/s ranges) between June and December, which marks the beginning of the dry season and the start of the rainy season, with a tendency for speed to decrease from January onwards. Monthly temperature averages tend to decrease continuously from December until reaching their minimum value in May (Figure 4a) and from this month onwards, with an increasing trend until August, coinciding with the maximum photosynthetically active radiation (Figure 4c). The maximum evapotranspiration occurs in September (Figure 4b), the same period in which the minimum rainfall occurs (Figure 4d), with the highest rainfall in April (around 400 mm). The statistical summary of the data by season is presented in Table 2. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{2}{c}{**Dry Season**} & \multicolumn{4}{c}{**Wet Season**} \\ \cline{2-7}  & **Mean** & **Sd** & **Max** & **Mean** & **Sd** & **Max** \\ \hline T\({}_{\text{air}}\) (\({}^{\circ}\)C) & 27.3 & \(\pm\)3.0 & 31.9 & 23.3 & \(\pm\)1.6 & 35.31 \\ PPFD (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) & 772.5 & \(\pm\)391.0 & 1667.5 & 9.1 & \(\pm\)70.6 & 1391.9 \\ Rainfall (mm) & 3.3 & \(\pm\)8.2 & 77.7 & 10.2 & \(\pm\)14.7 & 113.4 \\ ET (mm) & 1.5 & \(\pm\)0.46 & 3.0 & 1.2 & \(\pm\)0.56 & 2.5 \\ R\({}_{\text{n}}\) (W m\({}^{-2}\)) & 328.1 & \(\pm\)208.6 & 802.0 & 12.0 & \(\pm\)24.1 & 151.2 \\ LE (W m\({}^{-2}\)) & 202.0 & \(\pm\)132.9 & 745.0 & 5.1 & \(\pm\)11.7 & 160.1 \\ H (W m\({}^{-2}\)) & 67.7 & \(\pm\)58.7 & 417.9 & 12.4 & \(\pm\)11.6 & 267.9 \\ NDVI & 0.83 & \(\pm\)0.02 & 0.88 & 0.80 & \(\pm\)0.04 & 0.86 \\ EVI & 0.51 & \(\pm\)0.07 & 0.64 & 0.46 & \(\pm\)0.07 & 0.70 \\ GPP (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) & 12.4 & \(\pm\)3.1 & 19.4 & 13.3 & \(\pm\)5.11 & 1.12 \\ NEE (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) & \(-\)1.14 & \(\pm\)1.8 & 4.1 & \(-\)2.4 & \(\pm\)2.9 & 6.7 \\ RECO (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) & 10.13 & \(\pm\)3.4 & 18.8 & 8.5 & \(\pm\)3.0 & 15.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Seasonal midday average of meteorological variables, carbon variables and energy balance components. GPP is from the eddy covariance method. Rainfall and ET are accumulated for each period; EVI and NDVI are 16-day averages. Figure 3: Annual wind patterns at the Caxiuana site. The three main components of the ecosystem's energy flux are shown in Figure 5. Net radiation (Rn) was the main energy input to the ecosystem studied and drove other processes, such as evapotranspiration, the ecosystem's gross productivity, and temperature changes.