skills.rest
xjtulyc
xjtulycCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

era5-climate

Download ERA5 reanalysis data and compute anomaly and trend maps.

33|6|Updated Mar 17, 2026
Search Tags:#xarray#trend detection#era5#climate analysis#anomaly computation#cartopy mapping#copernicus cds api
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill era5-climate
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: era5-climate
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/04-earth-science/era5-climate
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill era5-climate

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires cdsapi, xarray, numpy, scipy, matplotlib, cartopy, pymannkendall, netCDF4, cfgrib, pandas.

What problem does it solve?

This skill removes the heavy lifting of acquiring ERA5 climate reanalysis data and turning it into interpretable anomaly and trend results with publication-ready maps.

Core Features & Use Cases

  • ERA5 data download via Copernicus CDS API: Pull monthly single-level or pressure-level reanalysis fields for chosen variables, years, and regions.
  • Climate anomaly computation: Compute monthly anomalies relative to a configurable baseline period (e.g., 1981–2010).
  • Grid-point trend analysis: Run pixel-wise Mann-Kendall tests with Theil-Sen slope estimation and produce significance masks.
  • Cartopy visualization: Render global or regional anomaly/trend maps, optionally stippling statistically significant areas.
  • Use case: Build an ERA5 temperature anomaly map and quantify where and when trends are increasing or decreasing for a region of interest.

Quick Start

Ask the AI to download ERA5 2-metre temperature for 1979–2023 globally, compute anomalies relative to 1991–2020, run Mann-Kendall trend analysis, and save a Robinson-projection anomaly map for a chosen year.

Frequently Asked Questions about era5-climate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I download ERA5 reanalysis data and compute climate anomalies using Python?▼

To compute ERA5 climate anomalies, you download monthly reanalysis fields via the Copernicus CDS API and calculate deviations relative to a configurable baseline period using xarray and NetCDF processing tools.

How do I run pixel-wise Mann-Kendall trend analysis on climate data?▼

You run pixel-wise Mann-Kendall trend analysis by applying the pymannkendall package to gridded climate arrays, producing Theil-Sen slope estimates and significance masks to identify statistically significant trends.

Can I create publication-ready climate anomaly maps with Cartopy?▼

Yes, you can create publication-ready climate anomaly maps with Cartopy by rendering global or regional projections, such as Robinson, and optionally stippling areas of statistically significant trends.

Do I need a Copernicus CDS API key to download ERA5 data?▼

Yes, you need an active Copernicus CDS API key and the cdsapi Python package configured in your environment to authenticate and retrieve ERA5 reanalysis fields for climate analysis.

What is the best way to analyze ERA5 temperature trends over a specific region?▼

The best way to analyze regional ERA5 temperature trends is to download subsetted reanalysis data, compute baseline anomalies, and run grid-point trend testing with Theil-Sen slopes to quantify where trends are increasing or decreasing.

Why use xarray and NetCDF4 for ERA5 climate data processing?▼

You use xarray and NetCDF4 for ERA5 climate data processing because they efficiently handle multidimensional gridded arrays, enabling seamless slicing, baseline anomaly computation, and statistical trend analysis.

Related Skills

Explore More Education & Research Skills→
iri-pycptiri-pycpt
3

PyCPT2-Seasonal-Forecast-User-Guide

Implement NextGen seasonal climate forecasting with PyCPT 2.5.

Official
Advanced
xjtulycxjtulyc
33

climate-trends

Compute climate trends and ETCCDI extreme indices with Mann-Kendall and Sen's slope.

Community
Intermediate
giadaf-booshagiadaf-boosha
10

statistical-analysis

Perform descriptive statistics, trend analysis, outlier detection, and hypothesis testing with Python.

Community
Advanced
Lord1EgyptLord1Egypt
2

climate-data-analysis

Process NetCDF and GRIB climate datasets for climatology, spatial aggregation, and visualization.

Community
Advanced