dianaveronez.github.io

Diana_CLIM680_project

Relationship between ENSO and Precipitation

Introduction

Precipitation is a critical component of the Earth's hydrological cycle, influencing water resources, agricultural productivity, and the frequency and intensity of extreme weather events. Understanding precipitation patterns is essential for managing water resources, predicting flood risks, and mitigating the effects of climate change (Smith et al., 2020).

One key factor influencing global precipitation variability is the El Niño-Southern Oscillation (ENSO). ENSO is a coupled ocean-atmosphere phenomenon that drives periodic changes in sea surface temperatures in the tropical Pacific Ocean, significantly affecting global climate systems. During El Niño events, shifts in atmospheric circulation alter rainfall patterns, leading to increased flooding in some regions and droughts in others (Trenberth et al., 2007). Conversely, La Niña events often cause contrasting precipitation anomalies, amplifying the complexity of hydrological responses worldwide (McPhaden et al., 2021).

In the context of Arlington County, Virginia, understanding the interplay between precipitation and ENSO is particularly vital for assessing flood risks. With its urbanized landscape and proximity to water bodies, Arlington is highly susceptible to flooding, especially during extreme precipitation events exacerbated by ENSO-related climate variability. Developing a nuanced understanding of these dynamics can enhance flood risk management, improve urban planning, and foster community resilience (County Climate Action Team, 2023).

Data

Nino 3.4 index

The Nino3.4 index can be obtained from the NOAA Physical Sciences Laboratory’s “Climate Indices: Monthly Atmospheric and Ocean Time Series” pages. The index called Nino3.4 is used to quantify the El Niño–Southern Oscillation (ENSO). When the index is significantly positive, it indicates an El Niño event. Conversely, when the index is significantly negative, it reflects a La Niña event. Values near zero are considered neutral conditions. The Nino3.4 index is derived from sea surface temperature (SST) anomalies in a specific region of the Tropical Pacific. The data can be downloaded from the following link

Precipitation Data

The Precipitation data can be obtained from the Global Precipitation Climatology Project (GPCP).

• Date: 1979-01-01 00:00:00 UTC to 2021-12-01 00:00:00 UTC. This study focuses on the dataset spans from 2020 to 2018.
• Model: GPCP - Global Precipitation Climatology Project, Monthly Precipitation Data.

The data can be downloaded from NOAA repository. link1 or link2

https://github.com/dianaveronez/Diana_CLIM680_project/blob/main/README.md

Code description and results

Project Notebook via Github

The files about this project you can find in the link.

Jupyter Notebooks: Part1 and Part2

Conda Environment

The climate2.yml file defines the environment needed to run all code successfully.

Libraries

In this project, the main libraries used on the notebook were:'xarray', 'pandas','numpy', 'matplotlib.pyplot', 'cartopy.crs', 'cartopy.mpl.ticker', 'calendar', 'cartopy.util', 'imageio', 'scipy.stats.ttest_ind'.

Monthly Mean Precipitation from 2000 - 2018

             Picture 01: Monthly Mean Precipitation Global View

             Picture 02: Monthly Mean Precipitation VA View

Nino3.4 Index

The ENSO can affect the precipitation as highlighted in the introduction. In the next steps, we will define El Nino, La Nina, or Neutral from 2000 until 2018.

                Picture 03: Nino 3.4 Indes for 1985 - 2020

To continue, the script analyzes sea surface temperature (SST) data to classify periods as El Nino, La Nina, or Neutral from 2000 to 2018. It performs the following tasks:

1. Classifies SST data into El Nino, La Nina, and Neutral categories based on temperature thresholds.
2. Counts the number of time points in each category.
3. Plots the SST data over time, highlighting El Nino, La Nina, and Neutral periods with different colors. Variables:
   • elnino: SST data points classified as El Nino (SST >= 0.5).
   • lanina: SST data points classified as La Nina (SST <= -0.5).
   • neutral: SST data points classified as Neutral (-0.5 < SST < 0.5).
   • counts: List containing the counts of time points in El Nino, La Nina, and Neutral categories. Plot:

• The SST data is plotted over time with different colors indicating El Nino (red), La Nina (blue), and Neutral (green) periods.

The next picture shows the results between 2000 - 2018 with El Nino, La Nina, and Neutral Periods.

         Picture 04: Niño 3.4 Index for 2000 to 2018 with El Nino, La Nina, and Neutral.

These analyses show that during the period we had 53 El Nino, 110 Neutral, and 63 La Nina.

Composite Precipitation Anomalies

The Global Precipitation Climatology Project (GPCP) was loaded to calculate the mean precipitation anomalies for El Niño, La Niña, and neutral conditions from a dataset.

The code calculates the mean precipitation anomalies for El Niño, La Niña, and neutral conditions from a dataset.

Steps:

1. Select the time periods corresponding to El Niño, La Niña, and neutral conditions from the dataset.
2. Calculate the mean precipitation anomalies for each condition.
3. Stores the results in a list comp_precip.
4. Prints the list.

          Picture 05: Composite Precipitation Anomalies during ENSO 2000-2018

Differences

The code calculates the differences in mean precipitation anomalies between El Niño and Neutral conditions, and La Niña and Neutral conditions.So, the first subplot shows the difference in mean precipitation anomalies between El Niño and Neutral conditions, the second subplot shows the difference in mean precipitation anomalies between La Niña and Neutral conditions.

          Picture 06: Composite Precipitation Differences during ENSO 2000-2018

T-Statistic

The code performs a two-sample t-test to compare mean precipitation anomalies between El Niño and Neutral conditions and La Niña and Neutral conditions. Steps:

1. Select precipitation values during El Niño and Neutral periods from the dataset.
2. Perform a two-sample t-test using the ttest_ind function from scipy.stats.
3. Calculate the t-statistic value and p-value.
4. Set the confidence level and check if the result is statistically significant.
5. Print the t-statistic, p-value, and significance of the result.

• The difference between El Niño and Neutral conditions is statistically significant.
• The difference between La Niña and Neutral conditions is not statistically significant.

Difference between composites and also mark where it is significant.

         Picture 07:  Composite Precipitation Differences

Summary

The project explores the relationship between ENSO (El Niño-Southern Oscillation) and precipitation, using datasets like the Niño 3.4 Index and GPCP precipitation data. It calculates and visualizes anomalies, composites, and differences in precipitation during El Niño, La Niña, and neutral phases. Statistical methods, including T-tests, assess the significance of these differences.

In conclusion, the project highlights the interplay between ENSO and precipitation, emphasizing global and regional effects. Precipitation is influenced by ENSO, with El Niño showing significant global impacts, as demonstrated in the global visualization. However, in Arlington, the influence of El Niño is present but not statistically significant, as shown in picture 07. This analysis underscores the importance of localized studies to complement global findings, providing critical insights for regional climate adaptation and resilience planning.

References

Smith, J., Brown, K., & Lee, R. (2020). Hydrology in a changing world: Managing water resources. Springer. Trenberth, K. E., et al. (2007). The impacts of El Niño and La Niña on precipitation patterns. Journal of Climate Science, 20(1), 123-135. McPhaden, M. J., et al. (2021). ENSO and global hydrology: A review. Nature Climate Change, 11(3), 181-190. County Climate Action Team. (2023). Arlington County climate risk assessment report. Available at: Arlington County Report Murray, D., et al., (2020) Facility for Weather and Climate Assessments (FACTS): A Community Resource for Assessing Weather and Climate Variability. Bull. Amer. Meteor. Soc., 101, E1214–E1224, doi: 10.1175/BAMS-D-19-0224.1 Adler et al. (2016) An Update (Version 2.3) of the GPCP Monthly Analysis. (in Preparation). Huffman, G.J., R.F. Adler, P. Arkin, A. Chang, R. Ferraro, A. Gruber, J. Janowiak, A. McNab, B. Rudolf, U. Schneider, 1997: The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset. Bull. Amer. Meteor. Soc., 78(1), 5-20.

Acknowledgements

I would like to thank the professor for coding assistance and answering questions.

https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax