--- name: climate-science-guide-d9b2bbac461c description: "Climate data analysis, modeling workflows, and carbon neutrality research met..." metadata: openclaw: emoji: "☁️" category: "domains" subcategory: "geoscience" keywords: ["climate change", "carbon neutrality", "atmospheric science", "climatology", "climate modeling"] source: "wentor" --- # Climate Science Guide A research skill for analyzing climate data, working with climate model outputs, and conducting carbon-related studies. Covers data sources, standard analytical workflows, and visualization techniques used in climate science publications. ## Climate Data Sources ### Observational Datasets | Dataset | Variables | Resolution | Period | Source | |---------|-----------|-----------|--------|--------| | ERA5 | Temperature, precipitation, wind, etc. | 0.25 deg, hourly | 1940-present | ECMWF/Copernicus | | GPCP | Precipitation | 2.5 deg, monthly | 1979-present | NASA | | HadCRUT5 | Surface temperature anomaly | 5 deg, monthly | 1850-present | Met Office | | NOAA GHCN | Station temperature, precipitation | Point data | 1850-present | NOAA | | CRU TS | Temperature, precipitation, vapor pressure | 0.5 deg, monthly | 1901-present | UEA CRU | ### CMIP6 Model Outputs ```python import xarray as xr def load_cmip6_data(model: str, experiment: str, variable: str, member: str = 'r1i1p1f1') -> xr.Dataset: """ Load CMIP6 model output from a local or cloud archive. Args: model: Model name (e.g., 'CESM2', 'UKESM1-0-LL') experiment: SSP scenario (e.g., 'ssp245', 'ssp585', 'historical') variable: Variable name (e.g., 'tas', 'pr', 'tos') member: Ensemble member ID """ # Using Pangeo cloud catalog import intake catalog = intake.open_esm_datastore( "https://storage.googleapis.com/cmip6/pangeo-cmip6.json" ) query = catalog.search( source_id=model, experiment_id=experiment, variable_id=variable, member_id=member, table_id='Amon' # Monthly atmospheric data ) ds = query.to_dataset_dict(zarr_kwargs={'consolidated': True}) key = list(ds.keys())[0] return ds[key] ``` ## Temperature Trend Analysis ### Computing Global Mean Temperature Anomaly ```python import numpy as np def compute_global_mean_anomaly(ds: xr.Dataset, var: str = 'tas', baseline: tuple = (1850, 1900)) -> xr.DataArray: """ Compute area-weighted global mean temperature anomaly relative to a baseline period. """ # Area weighting by latitude weights = np.cos(np.deg2rad(ds.lat)) weights = weights / weights.sum() # Global mean global_mean = ds[var].weighted(weights).mean(dim=['lat', 'lon']) # Baseline climatology baseline_mean = global_mean.sel( time=slice(str(baseline[0]), str(baseline[1])) ).mean('time') anomaly = global_mean - baseline_mean return anomaly # Usage # anomaly = compute_global_mean_anomaly(historical_ds) # anomaly.plot() # produces a time series of temperature anomaly ``` ## Carbon Budget Analysis ### Emissions and Remaining Budget Track cumulative CO2 emissions against the remaining carbon budget for temperature targets: ```python def carbon_budget_tracker(cumulative_emissions_gtco2: float, target_warming: float = 1.5) -> dict: """ Estimate remaining carbon budget. Based on IPCC AR6 estimates. """ # IPCC AR6 remaining budget from 2020 (GtCO2) budgets = { 1.5: {'50pct': 500, '67pct': 400, '83pct': 300}, 2.0: {'50pct': 1350, '67pct': 1150, '83pct': 900} } budget = budgets[target_warming] remaining = {prob: val - cumulative_emissions_gtco2 for prob, val in budget.items()} # At ~40 GtCO2/year current rate years_left = {prob: max(0, val / 40) for prob, val in remaining.items()} return {'remaining_budget_GtCO2': remaining, 'years_at_current_rate': years_left} result = carbon_budget_tracker(cumulative_emissions_gtco2=200, target_warming=1.5) print(result) ``` ## Climate Visualization ### Spatial Maps with Cartopy ```python import matplotlib.pyplot as plt import cartopy.crs as ccrs def plot_climate_map(data: xr.DataArray, title: str, cmap: str = 'RdBu_r', vmin: float = None, vmax: float = None): """Publication-quality climate map.""" fig = plt.figure(figsize=(12, 6)) ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson()) ax.coastlines(linewidth=0.5) ax.gridlines(draw_labels=True, linewidth=0.3, alpha=0.5) im = data.plot(ax=ax, transform=ccrs.PlateCarree(), cmap=cmap, vmin=vmin, vmax=vmax, add_colorbar=False) cbar = plt.colorbar(im, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7) cbar.set_label(data.attrs.get('units', '')) ax.set_title(title, fontsize=14) plt.tight_layout() return fig ``` ## Best Practices - Always report uncertainties: use multi-model ensembles and provide confidence intervals - Document data preprocessing steps for reproducibility - Use standardized calendar handling (`cftime`) for model outputs with non-standard calendars - Apply bias correction (e.g., quantile mapping) when comparing model outputs to observations - Follow FAIR data principles and cite datasets using their DOIs --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/domains/geoscience/climate-science-guide/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/domains/geoscience/climate-science-guide/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research Roles: Data Scientist, Researcher Specialties: API, Database Tools: Intake Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 08d04ae49b5b21514b063ad051061f7bf1f348862ba46a2c0e941bc1ae27ba57