<table>
   <tr>
    <td>CO</td>
    <td>carbon monoxide</td>
   </tr>
   <tr>
    <td>CO<sub>2</sub></td>
    <td>carbon dioxide</td>
   </tr>
   <tr>
    <td>COGL</td>
    <td>center of gravity</td>
   </tr>
   <tr>
    <td>COSMIC</td>
    <td>Constellation Observing System for Meteorology, Ionosphere, and Climate</td>
   </tr>
   <tr>
    <td>COVID-19</td>
    <td>Coronavirus disease 2019</td>
   </tr>
   <tr>
    <td>C<sub>p</sub></td>
    <td>specific heat capacity of air</td>
   </tr>
   <tr>
    <td>CPT</td>
    <td>cold-point tropopause</td>
   </tr>
   <tr>
    <td>CREATE</td>
    <td>Collaborative Research and Training Experience</td>
   </tr>
   <tr>
    <td>CrIS</td>
    <td>Cross-Track Infrared Sounder</td>
   </tr>
   <tr>
    <td>CRU TS</td>
    <td>Climatic Research Unit terrestrial series</td>
   </tr>
   <tr>
    <td>CRUTEM5</td>
    <td>Climatic Research Unit temperature version 5</td>
   </tr>
   <tr>
    <td>CSIC</td>
    <td>Spanish National Research Council</td>
   </tr>
   <tr>
    <td>CSSP</td>
    <td>Climate Science for Service Partnership</td>
   </tr>
   <tr>
    <td>D</td>
    <td>Germany</td>
   </tr>
   <tr>
    <td>DE</td>
    <td>Germany</td>
   </tr>
   <tr>
    <td>DLR</td>
    <td>German Aerospace Center</td>
   </tr>
   <tr>
    <td>DOE</td>
    <td>U.S. Department of Energy</td>
   </tr>
   <tr>
    <td>DOML</td>
    <td>day of maximum concentration</td>
   </tr>
   <tr>
    <td>DU</td>
    <td>Dobson unit</td>
   </tr>
   <tr>
    <td>EAST_SIB</td>
    <td>East Siberia</td>
   </tr>
   <tr>
    <td>EBAF</td>
    <td>Energy Balanced and Filled</td>
   </tr>
   <tr>
    <td>EC</td>
    <td>El Chichón</td>
   </tr>
   <tr>
    <td>ECMWF</td>
    <td>European Center for Medium-Range Weather Forecasts</td>
   </tr>
   <tr>
    <td>ECOSTRESS</td>
    <td>Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station</td>
   </tr>
   <tr>
    <td>EESC</td>
    <td>equivalent effective stratospheric chlorine</td>
   </tr>
   <tr>
    <td>EESC[A]</td>
    <td>equivalent effective stratospheric chlorine calculated for air representative of the Antarctic</td>
   </tr>
   <tr>
    <td>EESC[ML]</td>
    <td>equivalent effective stratospheric chlorine calculated for air representative of the midlatitude stratosphere</td>
   </tr>
   <tr>
    <td>ENSO</td>
    <td>El Niño–Southern Oscillation</td>
   </tr>
   <tr>
    <td>EOCIS</td>
    <td>Earth Observation Climate Information Service</td>
   </tr>
   <tr>
    <td>EOS</td>
    <td>end of season</td>
   </tr>
   <tr>
    <td>ER</td>
    <td>extinction ratio</td>
   </tr>
   <tr>
    <td>ERB</td>
    <td>Earth radiation budget</td>
   </tr>
   <tr>
    <td>ERC</td>
    <td>European Research Council</td>
   </tr>
   <tr>
    <td>ERF</td>
    <td>effective radiative forcing</td>
   </tr>
   <tr>
    <td>ESA</td>
    <td>European Space Agency</td>
   </tr>
   <tr>
    <td>ESSIC</td>
    <td>Earth System Science Interdisciplinary Center</td>
   </tr>
   <tr>
    <td>ET</td>
    <td>evapotranspiration</td>
   </tr>
   <tr>
    <td>EUMETSAT</td>
    <td>European Organisation for the Exploitation of Meteorological Satellites</td>
   </tr>
   <tr>
    <td>EUR</td>
    <td>Europe</td>
   </tr>
   <tr>
    <td>FAPAR</td>
    <td>fraction of absorbed photosynthetically active radiation</td>
   </tr>
   <tr>
    <td>FBD</td>
    <td>full bloom date</td>
   </tr>
   <tr>
    <td>FF</td>
    <td>fossil fuel</td>
   </tr>
   <tr>
    <td>FLASHFlux</td>
    <td>Fast Longwave and Shortwave Radiative Fluxes</td>
   </tr>
   <tr>
    <td>FOC</td>
    <td>Fisheries and Oceans Canada</td>
   </tr>
   <tr>
    <td>FP</td>
    <td>frost point</td>
   </tr>
   <tr>
    <td>FR</td>
    <td>France</td>
   </tr>
   <tr>
    <td>GCOS</td>
    <td>Global Climate Observing System</td>
   </tr>
   <tr>
    <td>GEOS-CF</td>
    <td>Goddard Earth Observing System Composition Forecast Modeling System</td>
   </tr>
   <tr>
    <td>GFAS</td>
    <td>Global Fire Assimilation System</td>
   </tr>
   <tr>
    <td>GFED4S</td>
    <td>Global Fire Emissions Database version 4.1</td>
   </tr>
  </table>
<table>
   <tr>
    <td>GHCN</td>
    <td>Global Historical Climatology Network</td>
   </tr>
   <tr>
    <td>GHCNDEX</td>
    <td>Global Historical Climatology Network Daily Extremes</td>
   </tr>
   <tr>
    <td>GHCNh</td>
    <td>Global Historical Climatology Network hourly</td>
   </tr>
   <tr>
    <td>GHG</td>
    <td>greenhouse gas</td>
   </tr>
   <tr>
    <td>GISS</td>
    <td>Goddard Institute for Space Studies</td>
   </tr>
   <tr>
    <td>GISTEMP</td>
    <td>Goddard Institute for Space Studies Surface Temperature Analysis</td>
   </tr>
   <tr>
    <td>GLEAM4</td>
    <td>Global Land Evaporation Amsterdam Model version 4</td>
   </tr>
   <tr>
    <td>GLM</td>
    <td>Geostationary Lightning Mapper</td>
   </tr>
   <tr>
    <td>GLOBE</td>
    <td>Global Learning and Observations to Benefit the Environment</td>
   </tr>
   <tr>
    <td>GloFASv4</td>
    <td>Global Flood Awareness System version 4</td>
   </tr>
   <tr>
    <td>GloSSAC</td>
    <td>Global Space-based Stratospheric Aerosol Climatology</td>
   </tr>
   <tr>
    <td>GMI</td>
    <td>Global Precipitation Measurement Microwave Imager</td>
   </tr>
   <tr>
    <td>GMST</td>
    <td>global mean surface temperature</td>
   </tr>
   <tr>
    <td>GNSS</td>
    <td>Global Navigation Satellite System</td>
   </tr>
   <tr>
    <td>GNSS-RO</td>
    <td>Global Navigation Satellite System Radio Occultation</td>
   </tr>
   <tr>
    <td>GO</td>
    <td>ground observation</td>
   </tr>
   <tr>
    <td>GOES</td>
    <td>Geostationary Operational Environmental Satellite</td>
   </tr>
   <tr>
    <td>GOSAT</td>
    <td>Greenhouse gases Observing SATellite</td>
   </tr>
   <tr>
    <td>GOZCARDS</td>
    <td>Global Ozone Chemistry and Related Trace Gas Data records for the Stratosphere</td>
   </tr>
   <tr>
    <td>GPCC</td>
    <td>Global Precipitation Climatology Centre</td>
   </tr>
   <tr>
    <td>GPCP</td>
    <td>Global Precipitation Climatology Project</td>
   </tr>
   <tr>
    <td>GRACE</td>
    <td>Gravity Recovery and Climate Experiment</td>
   </tr>
   <tr>
    <td>GRACE-FO</td>
    <td>Gravity Recovery and Climate Experiment Follow-On</td>
   </tr>
   <tr>
    <td>GREALM</td>
    <td>Global Reservoirs and Lakes Monitor</td>
   </tr>
   <tr>
    <td>GSL</td>
    <td>Global Snow Lab</td>
   </tr>
   <tr>
    <td>Gt</td>
    <td>gigaton</td>
   </tr>
   <tr>
    <td>GTO-ECV</td>
    <td>Global Ozone Monitoring Experiment-type Total Ozone Essential Climate Variable</td>
   </tr>
   <tr>
    <td>GVA</td>
    <td>Generalitat Valenciana</td>
   </tr>
   <tr>
    <td>HadCRUT5</td>
    <td>Met Office Hadley Centre/Climatic Research Unit Temperature version 5</td>
   </tr>
   <tr>
    <td>HadEX3</td>
    <td>Met Office Hadley Centre Extremes Dataset version 3</td>
   </tr>
   <tr>
    <td>HadISD3</td>
    <td>Met Office Hadley Centre Integrated Surface Dataset 3</td>
   </tr>
   <tr>
    <td>HadISDH</td>
    <td>Met Office Hadley Centre Integrated Surface Dataset of Humidity</td>
   </tr>
   <tr>
    <td>HadISDH.blend</td>
    <td>Met Office Hadley Centre Integrated Surface Dataset of Humidity over Land and Ocean</td>
   </tr>
   <tr>
    <td>HadISDH/extremes</td>
    <td>Met Office Hadley Centre Integrated Surface Dataset of Humidity Extremes</td>
   </tr>
   <tr>
    <td>HadISDH.land</td>
    <td>Met Office Hadley Centre Integrated Surface Dataset of Humidity over Land</td>
   </tr>
   <tr>
    <td>HadISDH.marine</td>
    <td>Met Office Hadley Centre Integrated Surface Dataset of Humidity over Ocean</td>
   </tr>
   <tr>
    <td>HadSST4</td>
    <td>Met Office Hadley Centre Sea Surface Temperature Dataset version 4</td>
   </tr>
   <tr>
    <td>HCFC</td>
    <td>hydrochlorofluorocarbon</td>
   </tr>
   <tr>
    <td>HFC</td>
    <td>hydrofluorocarbon</td>
   </tr>
   <tr>
    <td>HIRS</td>
    <td>High-resolution Infrared Radiation Sounder</td>
   </tr>
   <tr>
    <td>HNO<sub>3</sub></td>
    <td>nitric acid</td>
   </tr>
   <tr>
    <td>Hu</td>
    <td>Hunga</td>
   </tr>
   <tr>
    <td>HYSPLIT</td>
    <td>Hybrid Single-Particle Lagrangian Integrated Trajectory</td>
   </tr>
   <tr>
    <td>IAK_CMV</td>
    <td>Interior Alaska and central Mackenzie Valley, Northwest Territories</td>
   </tr>
   <tr>
    <td>IC</td>
    <td>in-cloud</td>
   </tr>
   <tr>
    <td>ICESat-2</td>
    <td>Ice, Cloud, and land Elevation Satellite 2</td>
   </tr>
   <tr>
    <td>INRAE</td>
    <td>National Institute of Agricultural Research</td>
   </tr>
   <tr>
    <td>INSU</td>
    <td>National Institute of Sciences of the Universe</td>
   </tr>
   <tr>
    <td>IOD</td>
    <td>Indian Ocean dipole</td>
   </tr>
   <tr>
    <td>IR</td>
    <td>infrared</td>
   </tr>
   <tr>
    <td>ISF</td>
    <td>Israel Science Foundation</td>
   </tr>
   <tr>
    <td>ISPF</td>
    <td>International Science Partnerships Fund</td>
   </tr>
  </table>
<table>
   <tr>
    <td>ISS</td>
    <td>International Space Station</td>
   </tr>
   <tr>
    <td>IT</td>
    <td>Italy</td>
   </tr>
   <tr>
    <td>ITCZ</td>
    <td>Intertropical Convergence Zone</td>
   </tr>
   <tr>
    <td>JMA</td>
    <td>Japanese Meteorological Agency</td>
   </tr>
   <tr>
    <td>JPSS</td>
    <td>Joint Polar Satellite System</td>
   </tr>
   <tr>
    <td>JRA-3Q</td>
    <td>Japanese Reanalysis for Three Quarters of a Century</td>
   </tr>
   <tr>
    <td>JRA-55</td>
    <td>Japanese 55-year Reanalysis</td>
   </tr>
   <tr>
    <td>JRC</td>
    <td>Joint Research Centre</td>
   </tr>
   <tr>
    <td>K</td>
    <td>Kelvin</td>
   </tr>
   <tr>
    <td>Ka</td>
    <td>Kasatochi</td>
   </tr>
   <tr>
    <td>KAUST</td>
    <td>King Abdullah University of Science and Technology</td>
   </tr>
   <tr>
    <td>KOMPSAT-5</td>
    <td>Korea Multi-Purpose Satellite-5</td>
   </tr>
   <tr>
    <td>L</td>
    <td>latent heat of vaporization</td>
   </tr>
   <tr>
    <td>LATMOS</td>
    <td>Laboratory for Atmospheres, Observations, and Space</td>
   </tr>
   <tr>
    <td>LEO</td>
    <td>Office of Low Earth Orbit</td>
   </tr>
   <tr>
    <td>LI</td>
    <td>Lightning Imager</td>
   </tr>
   <tr>
    <td>LINCGLOBAL</td>
    <td>International Global Change Laboratory</td>
   </tr>
   <tr>
    <td>LiO3s</td>
    <td>Stratospheric Ozone Lidar at Haute-Provence Observatory</td>
   </tr>
   <tr>
    <td>LLGHG</td>
    <td>long-lived greenhouse gases</td>
   </tr>
   <tr>
    <td>LLNL</td>
    <td>Lawrence Livermore National Laboratory</td>
   </tr>
   <tr>
    <td>LOWESS</td>
    <td>locally weighted scatterplot smoothing</td>
   </tr>
   <tr>
    <td>LS</td>
    <td>lower stratosphere</td>
   </tr>
   <tr>
    <td>LST</td>
    <td>land surface temperature</td>
   </tr>
   <tr>
    <td>LST_CCI</td>
    <td>Climate Change Initiative for Land Surface Temperature</td>
   </tr>
   <tr>
    <td>LSWT</td>
    <td>lake surface water temperature</td>
   </tr>
   <tr>
    <td>LTA</td>
    <td>Lidar Temperature Aerosol</td>
   </tr>
   <tr>
    <td>LTT</td>
    <td>lower-tropospheric temperature</td>
   </tr>
   <tr>
    <td>LWCRE</td>
    <td>longwave cloud radiative effect</td>
   </tr>
   <tr>
    <td>MABAK</td>
    <td>Methods for analyzing and assessing changes in the atmosphere and the climate system</td>
   </tr>
   <tr>
    <td>MACCity</td>
    <td>Monitoring Atmospheric Composition and Climate and CityZen</td>
   </tr>
   <tr>
    <td>MCM</td>
    <td>million cubic meters</td>
   </tr>
   <tr>
    <td>MEGAN</td>
    <td>Model of Emissions of Gases and Aerosols from Nature</td>
   </tr>
   <tr>
    <td>MERIS</td>
    <td>Medium Resolution Imaging Spectrometer</td>
   </tr>
   <tr>
    <td>MetOp</td>
    <td>Meteorological Operational satellite</td>
   </tr>
   <tr>
    <td>MLO</td>
    <td>Mauna Loa Observatory</td>
   </tr>
   <tr>
    <td>MLS</td>
    <td>Microwave Limb Sounder</td>
   </tr>
   <tr>
    <td>MNT_FRA</td>
    <td>French Alps</td>
   </tr>
   <tr>
    <td>MNT_IT</td>
    <td>Italian Alps</td>
   </tr>
   <tr>
    <td>MNT_NOR</td>
    <td>Norwegian mountains</td>
   </tr>
   <tr>
    <td>MNT_QTP</td>
    <td>Qinghai-Tibet Plateau</td>
   </tr>
   <tr>
    <td>MNT_SWI</td>
    <td>Swiss Alps</td>
   </tr>
   <tr>
    <td>MODIS</td>
    <td>Moderate Resolution Imaging Spectroradiometer</td>
   </tr>
   <tr>
    <td>MOPITT</td>
    <td>Measurement of Pollution in the Troposphere</td>
   </tr>
   <tr>
    <td>MRI</td>
    <td>Meteorological Research Institute</td>
   </tr>
   <tr>
    <td>MSLP</td>
    <td>mean sea-level pressure</td>
   </tr>
   <tr>
    <td>MSU</td>
    <td>Microwave Sounding Unit</td>
   </tr>
   <tr>
    <td>MSWEP</td>
    <td>Multi-Source Weighted-Ensemble Precipitation</td>
   </tr>
   <tr>
    <td>MTG</td>
    <td>Meteosat Third Generation</td>
   </tr>
   <tr>
    <td>MWR</td>
    <td>Microwave Radiometer</td>
   </tr>
   <tr>
    <td>N2O</td>
    <td>nitrous oxide</td>
   </tr>
   <tr>
    <td>Na</td>
    <td>Nabro</td>
   </tr>
   <tr>
    <td>NA</td>
    <td>North America</td>
   </tr>
  </table>
<table>
  <tr>
    <td>NAO</td>
    <td>North Atlantic Oscillation</td>
  </tr>
  <tr>
    <td>NCAS</td>
    <td>National Centre for Atmospheric Science</td>
  </tr>
  <tr>
    <td>NCEP</td>
    <td>National Centers for Environmental Prediction</td>
  </tr>
  <tr>
    <td>NDACC</td>
    <td>Network for the Detection for Stratospheric Change</td>
  </tr>
  <tr>
    <td>NERC</td>
    <td>Natural Environmental Research Council</td>
  </tr>
  <tr>
    <td>NH</td>
    <td>Northern Hemisphere</td>
  </tr>
  <tr>
    <td>NH<sub>3</sub></td>
    <td>ammonia</td>
  </tr>
  <tr>
    <td>NH<sub>4</sub>NO<sub>3</sub></td>
    <td>ammonium nitrate</td>
  </tr>
  <tr>
    <td>NIWA</td>
    <td>National Institute of Water and Atmospheric Research</td>
  </tr>
  <tr>
    <td>NL</td>
    <td>The Netherlands</td>
  </tr>
  <tr>
    <td>NMAT</td>
    <td>night marine air temperature</td>
  </tr>
  <tr>
    <td>NO</td>
    <td>Norway</td>
  </tr>
  <tr>
    <td>NO<sub>2</sub></td>
    <td>nitrogen dioxide</td>
  </tr>
  <tr>
    <td>NOAA Global Temp</td>
    <td>NOAA Merged Land Ocean Global Surface Temperature Analysis</td>
  </tr>
  <tr>
    <td>NOx</td>
    <td>nitrogen oxides</td>
  </tr>
  <tr>
    <td>NRT</td>
    <td>near-real-time</td>
  </tr>
  <tr>
    <td>NSERC</td>
    <td>Natural Sciences and Engineering Research Council of Canada</td>
  </tr>
  <tr>
    <td>NSF</td>
    <td>National Science Foundation</td>
  </tr>
  <tr>
    <td>NTL-LTER</td>
    <td>North Temperate Lakes Long-Term Ecological Research</td>
  </tr>
  <tr>
    <td>ODGI</td>
    <td>Ozone Depleting Gas Index</td>
  </tr>
  <tr>
    <td>ODS</td>
    <td>ozone-depleting substance</td>
  </tr>
  <tr>
    <td>OH</td>
    <td>hydroxyl radical</td>
  </tr>
  <tr>
    <td>OHP</td>
    <td>Haute-Provence Observatory</td>
  </tr>
  <tr>
    <td>OLA</td>
    <td>Observatory of alpine LAkes</td>
  </tr>
  <tr>
    <td>OLCI</td>
    <td>Ocean and Land Colour Instrument</td>
  </tr>
  <tr>
    <td>OLR</td>
    <td>outgoing longwave radiation</td>
  </tr>
  <tr>
    <td>OLS</td>
    <td>ordinary least-squares</td>
  </tr>
  <tr>
    <td>OMI</td>
    <td>Ozone Monitoring Instrument</td>
  </tr>
  <tr>
    <td>OMPS</td>
    <td>Ozone Mapping and Profiler Suite</td>
  </tr>
  <tr>
    <td>OMPS-LP</td>
    <td>Ozone Mapping and Profiler Suite Limb Profiler</td>
  </tr>
  <tr>
    <td>OMPS-NP</td>
    <td>Ozone Mapping and Profiler Suite Nadir Profile</td>
  </tr>
  <tr>
    <td>ONI</td>
    <td>Oceanic Niño Index</td>
  </tr>
  <tr>
    <td>OSIRIS-REx</td>
    <td>Origins, Spectral Interpretation, Resource Identification, and Security—Regolith Explorer</td>
  </tr>
  <tr>
    <td>OZCAR</td>
    <td>Critical Zone Observatories: Research and Application</td>
  </tr>
  <tr>
    <td>PAN</td>
    <td>Peroxyacetyl nitrate</td>
  </tr>
  <tr>
    <td>PANs</td>
    <td>Acyl peroxynitrates</td>
  </tr>
  <tr>
    <td>PC</td>
    <td>PhenoCam</td>
  </tr>
  <tr>
    <td>PDO</td>
    <td>Pacific Decadal Oscillation</td>
  </tr>
  <tr>
    <td>Permafrost_CCI</td>
    <td>Permafrost Climate Change Initiative</td>
  </tr>
  <tr>
    <td>PERMOS</td>
    <td>Swiss Permafrost Monitoring Network</td>
  </tr>
  <tr>
    <td>PGRR</td>
    <td>Proving Ground and Risk Reduction</td>
  </tr>
  <tr>
    <td>Pi</td>
    <td>Pinatubo</td>
  </tr>
  <tr>
    <td>PM2.5</td>
    <td>particulate matter</td>
  </tr>
  <tr>
    <td>PNA</td>
    <td>Pacific-North American</td>
  </tr>
  <tr>
    <td>PNE</td>
    <td>Pacific Northwest Event</td>
  </tr>
  <tr>
    <td>POPS</td>
    <td>Portable Optical Particle Spectrometer</td>
  </tr>
  <tr>
    <td>PSA</td>
    <td>Pacific-South American</td>
  </tr>
  <tr>
    <td>PTI-CLIMA</td>
    <td>Interdisciplinary Thematic Platform for Climate and Climate Services</td>
  </tr>
  <tr>
    <td>pyroCb</td>
    <td>pyrocumulonimbus</td>
  </tr>
  <tr>
    <td>q</td>
    <td>specific humidity</td>
  </tr>
  <tr>
    <td>QBO</td>
    <td>quasi-biennial oscillation</td>
  </tr>
  <tr>
    <td>QTP</td>
    <td>Qinghai-Tibet Plateau</td>
  </tr>
</table>
<table>
   <tr>
    <td>QuickSCAT</td>
    <td>Quick Scatterometer</td>
   </tr>
   <tr>
    <td>Ra</td>
    <td>Raikoke</td>
   </tr>
   <tr>
    <td>RATPAC</td>
    <td>Radiosonde Atmospheric Temperature Products for Assessing Climate</td>
   </tr>
   <tr>
    <td>RCP</td>
    <td>Representation Concentration Pathways</td>
   </tr>
   <tr>
    <td>RFaci</td>
    <td>aerosol-cloud interactions</td>
   </tr>
   <tr>
    <td>RFari</td>
    <td>aerosol-radiation interactions</td>
   </tr>
   <tr>
    <td>RGMA</td>
    <td>Regional and Global Model Analysis Program</td>
   </tr>
   <tr>
    <td>RGV</td>
    <td>rock glacier velocity</td>
   </tr>
   <tr>
    <td>RH</td>
    <td>relative humidity</td>
   </tr>
   <tr>
    <td>RSS</td>
    <td>Remote Sensing Systems</td>
   </tr>
   <tr>
    <td>RSW</td>
    <td>reflected shortwave</td>
   </tr>
   <tr>
    <td>Ru</td>
    <td>Ruang</td>
   </tr>
   <tr>
    <td>Rx1day</td>
    <td>one-day maxima</td>
   </tr>
   <tr>
    <td>Rx5day</td>
    <td>accumulated five-day maxima</td>
   </tr>
   <tr>
    <td>Sa</td>
    <td>Sarychev</td>
   </tr>
   <tr>
    <td>SAGE III</td>
    <td>Stratospheric Aerosol and Gas Experiment III</td>
   </tr>
   <tr>
    <td>SAM</td>
    <td>Southern Annular Mode</td>
   </tr>
   <tr>
    <td>sAOD</td>
    <td>stratospheric aerosol optical depth</td>
   </tr>
   <tr>
    <td>SAT</td>
    <td>surface high temperature</td>
   </tr>
   <tr>
    <td>SBUV</td>
    <td>Solar Backscatter Ultraviolet Radiometer</td>
   </tr>
   <tr>
    <td>SCE</td>
    <td>snow cover extent</td>
   </tr>
   <tr>
    <td>SCIAMACHY</td>
    <td>Scanning Imaging Absorption Spectrometer for Atmospheric Chartography</td>
   </tr>
   <tr>
    <td>scPDSI</td>
    <td>self-calibrating Palmer Drought Severity Index</td>
   </tr>
   <tr>
    <td>SeaWiFS</td>
    <td>Sea-Viewing Wide Field-of-View Sensor</td>
   </tr>
   <tr>
    <td>SEH</td>
    <td>super extreme hotspot</td>
   </tr>
   <tr>
    <td>Sh</td>
    <td>Shiveluch</td>
   </tr>
   <tr>
    <td>SH</td>
    <td>Southern Hemisphere</td>
   </tr>
   <tr>
    <td>SIOS</td>
    <td>Svalbard Integrated Earth Observing System</td>
   </tr>
   <tr>
    <td>SI-x</td>
    <td>Spring Index</td>
   </tr>
   <tr>
    <td>SJ</td>
    <td>Svalbard</td>
   </tr>
   <tr>
    <td>SK</td>
    <td>Slovakia</td>
   </tr>
   <tr>
    <td>SLCF</td>
    <td>short-lived climate forcer</td>
   </tr>
   <tr>
    <td>SLSTR/A</td>
    <td>Sea and Land Surface Temperature Radiometer onboard the Sentinel-3A platform</td>
   </tr>
   <tr>
    <td>SLSTR/B</td>
    <td>Sea and Land Surface Temperature Radiometer onboard the Sentinel-3B platform</td>
   </tr>
   <tr>
    <td>SNPP</td>
    <td>Suomi National Polar-orbiting Partnership</td>
   </tr>
   <tr>
    <td>SO₂</td>
    <td>sulfur dioxide</td>
   </tr>
   <tr>
    <td>SOERE</td>
    <td>Observation and Experimentation System for Environmental Research</td>
   </tr>
   <tr>
    <td>SOI</td>
    <td>Southern Oscillation Index</td>
   </tr>
   <tr>
    <td>SOS</td>
    <td>start of season</td>
   </tr>
   <tr>
    <td>SSF</td>
    <td>Single Scanner Footprint</td>
   </tr>
   <tr>
    <td>SSM/I</td>
    <td>Special Sensor Microwave/Imager</td>
   </tr>
   <tr>
    <td>SSMIS</td>
    <td>Special Sensor Microwave Imager/Sounder</td>
   </tr>
   <tr>
    <td>SST</td>
    <td>sea surface temperature</td>
   </tr>
   <tr>
    <td>SSU</td>
    <td>Stratospheric Sounding Unit</td>
   </tr>
   <tr>
    <td>SSW</td>
    <td>sudden stratospheric warming</td>
   </tr>
   <tr>
    <td>STP</td>
    <td>standard temperature and pressure</td>
   </tr>
   <tr>
    <td>SW</td>
    <td>shortwave</td>
   </tr>
   <tr>
    <td>SWCRE</td>
    <td>shortwave cloud radiative effect</td>
   </tr>
   <tr>
    <td>SWOOSH</td>
    <td>Stratospheric Water and OzOne Satellite Homogenized</td>
   </tr>
   <tr>
    <td>SWOT</td>
    <td>Surface Water and Ocean Topography</td>
   </tr>
   <tr>
    <td>T</td>
    <td>dry-bulb air temperature</td>
   </tr>
   <tr>
    <td>T12</td>
    <td>water vapor channel</td>
   </tr>
  </table>
<table>
   <tr>
    <td>T2</td>
    <td>upper-tropospheric temperature</td>
   </tr>
   <tr>
    <td>T2m</td>
    <td>near-surface air temperature at ~1.5 m<sup>-2</sup> m above the surface</td>
   </tr>
   <tr>
    <td>TCWV</td>
    <td>total column water vapor</td>
   </tr>
   <tr>
    <td>TDX</td>
    <td>TerraSAR-X add-on for Digital Elevation Measurement</td>
   </tr>
   <tr>
    <td>Teq</td>
    <td>equivalent temperature</td>
   </tr>
   <tr>
    <td>TISA</td>
    <td>Time Interpolated and Spatially Averaged</td>
   </tr>
   <tr>
    <td>TLS</td>
    <td>Lower-stratosphere temperatures</td>
   </tr>
   <tr>
    <td>TMI</td>
    <td>Tropical Rainfall Measuring Mission Microwave Imager</td>
   </tr>
   <tr>
    <td>TOA</td>
    <td>top-of-atmosphere</td>
   </tr>
   <tr>
    <td>TOB</td>
    <td>tropospheric ozone burden</td>
   </tr>
   <tr>
    <td>Tq</td>
    <td>latent temperature</td>
   </tr>
   <tr>
    <td>TRISHNA</td>
    <td>Thermal Infrared Imaging Satellite for High-resolution Natural Resource Assessment</td>
   </tr>
   <tr>
    <td>TRMM</td>
    <td>Tropical Rainfall Measuring Mission</td>
   </tr>
   <tr>
    <td>TROPOMI</td>
    <td>Tropospheric Monitoring Instrument</td>
   </tr>
   <tr>
    <td>TSI</td>
    <td>total solar irradiance</td>
   </tr>
   <tr>
    <td>TSX</td>
    <td>TerraSAR-X</td>
   </tr>
   <tr>
    <td>TTT</td>
    <td>tropical tropospheric temperatures</td>
   </tr>
   <tr>
    <td>T<sub>w</sub></td>
    <td>wet-bulb temperature</td>
   </tr>
   <tr>
    <td>T<sub>w</sub>N</td>
    <td>minimum humid-heat intensity</td>
   </tr>
   <tr>
    <td>TWS</td>
    <td>terrestrial water storage</td>
   </tr>
   <tr>
    <td>T<sub>w</sub>X</td>
    <td>humid-heat intensity</td>
   </tr>
   <tr>
    <td>TXx</td>
    <td>highest annual maximum temperature</td>
   </tr>
   <tr>
    <td>U.K.</td>
    <td>United Kingdom</td>
   </tr>
   <tr>
    <td>U.S.</td>
    <td>United States</td>
   </tr>
   <tr>
    <td>UACO</td>
    <td>Upper Atmosphere Composition Observations</td>
   </tr>
   <tr>
    <td>UAHNMAT</td>
    <td>University of Alabama in Huntsville Night Marine Air Temperature</td>
   </tr>
   <tr>
    <td>UFS</td>
    <td>Unified Forecast System</td>
   </tr>
   <tr>
    <td>UGLOS</td>
    <td>Upper Great Lakes Observing System</td>
   </tr>
   <tr>
    <td>UK-SCAPE</td>
    <td>United Kingdom Status, Change and Projections of the Environment</td>
   </tr>
   <tr>
    <td>UNIS</td>
    <td>University Centre in Svalbard</td>
   </tr>
   <tr>
    <td>USA NPN</td>
    <td>USA National Phenology Network</td>
   </tr>
   <tr>
    <td>UT</td>
    <td>upper troposphere</td>
   </tr>
   <tr>
    <td>UTH</td>
    <td>upper tropospheric humidity</td>
   </tr>
   <tr>
    <td>UV</td>
    <td>University of Valencia</td>
   </tr>
   <tr>
    <td>VIIRS</td>
    <td>Visible Infrared Imaging Radiometer Suite</td>
   </tr>
   <tr>
    <td>VOC</td>
    <td>volatile organic compound</td>
   </tr>
   <tr>
    <td>VOD</td>
    <td>vegetation optical depth</td>
   </tr>
   <tr>
    <td>VODCA</td>
    <td>Vegetation Optical Depth Climate Archive</td>
   </tr>
   <tr>
    <td>VWC</td>
    <td>vegetation water content</td>
   </tr>
   <tr>
    <td>w.e.</td>
    <td>water equivalent</td>
   </tr>
   <tr>
    <td>WGMS</td>
    <td>World Glacier Monitoring Service</td>
   </tr>
   <tr>
    <td>WMO</td>
    <td>World Meteorological Organization</td>
   </tr>
   <tr>
    <td>WOUDC</td>
    <td>World Ozone and Ultraviolet Radiation Data Centre</td>
   </tr>
   <tr>
    <td>WV</td>
    <td>water vapor</td>
   </tr>
   <tr>
    <td>YM</td>
    <td>yearly mean</td>
   </tr>
  </table>
# Appendix 2: Datasets and sources

## Section 2b Temperature

<table><thead><tr><td>Sub-section</td><td>General Variable or Phenomenon</td><td>Specific Dataset or Variable</td><td>Source</td></tr></thead><tbody><tr><td>2b1</td><td>Temperature, [Near] Surface</td><td>Berkeley Earth</td><td>https://berkeleyearth.org/data/</td></tr><tr><td>2b1, 2b4, 2b7</td><td>Temperature, [Near] Surface</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2b1</td><td>Temperature, [Near] Surface</td><td>HadCRUT5</td><td>https://www.metoffice.gov.uk/hadobs/hadcrut5/</td></tr><tr><td>2b1</td><td>Temperature, [Near] Surface</td><td>Climatic Research Unit Temperature Version 5 (CRUTEM5)</td><td>https://www.metoffice.gov.uk/hadobs/crutem5/</td></tr><tr><td>2b1, 2b3</td><td>Temperature, [Near] Surface</td><td>Met Office Hadley Centre Sea Surface Temperature Dataset (HadSST) Version 4</td><td>https://www.metoffice.gov.uk/hadobs/hadsst4/</td></tr><tr><td>2b1, 2b4, 2b7</td><td>Temperature, [Near] Surface</td><td>Japanese Reanalysis for Three Quarters of a Century (JRA-3Q)</td><td>https://search.diasjp.net/en/dataset/JRA3Q</td></tr><tr><td>2b1, 2b2</td><td>Temperature, [Near] Surface</td><td>NASA/Goddard Institute for Space Studies (GISS) Global Temperature Version 4</td><td>https://data.giss.nasa.gov/gistemp/</td></tr><tr><td>2b1</td><td>Temperature, [Near] Surface</td><td>NOAA/NCEI NOAAGlobalTemp</td><td>https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>Full Lake Surface Temperature Water Dataset</td><td>https://cds.climate.copernicus.eu/datasets/satellite-lake-water-temperature</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>National Buoy Data Center Great Lakes Buoys</td><td>https://www.ndbc.noaa.gov/mobile/region.php?reg=great_lakes</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>Balaton Lakes</td><td>https://odp.met.hu/climate/observations_hungary/hourly/historical/</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>Canadian Lakes</td><td>https://www.meds-sdmm.dfo-mpo.gc.ca/isdm-gdsi/waves-vagues/data-donnees/index-eng.asp</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>Biel and Thun Lakes (Switzerland); Biwa and Mikata Lakes (Japan)</td><td>https://portal.gemstat.org/applications/public.html?publicuser=PublicUser</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>Trout Lake</td><td>https://portal.edirepository.org/nis/metadataviewer?packageid=knb-iter-ntl.116.10</td></tr><tr><td>2b2</td><td>Lake Temperature</td><td>European Space Agency (ESA) Climate Change Initiative (CCI) LAKES Lake Surface Water Temperature (LSWT) Version 2.0.2</td><td>https://catalogue.ceda.ac.uk/uuid/a07deacaffb8453e93d57ee214676304</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2b3</td><td>Nighttime Marine Air Temperature</td><td>Climate Linked Atlantic Sector Science Night Marine Air Temperature (CLASSnmat)</td><td>https://catalogue.ceda.ac.uk/uuid/5bbf48b128bd488dbb10a56111feb36a</td></tr><tr><td>2b3</td><td>Nighttime Marine Air Temperature</td><td>University of Alabama in Huntsville Night Marine Air Temperature (UAHNMAT) Version 1</td><td>https://www.nsstc.uah.edu/climate/; https://doi.org/10.1002/joc.6354</td></tr><tr><td>2b4</td><td>Temperature, [Near] Surface</td><td>MERRA-2</td><td>https://gmao.gsfc.nasa.gov/gmao-products/merra-2/</td></tr><tr><td>2b4</td><td>Temperature, [Near] Surface</td><td>Global Historical Climatology Network Daily Extremes (GHCNDEX)</td><td>https://www.climdex.org/</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>MERRA-2</td><td>https://gmao.gsfc.nasa.gov/gmao-products/merra-2/</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>JRA-3Q</td><td>https://search.diasjp.net/en/dataset/JRA3Q</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>NOAA National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Applications and Research (STAR) Microwave Sounding Unit (MSU) Version 5</td><td>https://www.star.nesdis.noaa.gov/data/mscat/MSU_AMSU_v5.0/Monthly_Atmospheric_Layer_Mean_Temperature/</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>Radiosonde Observation Correction Using Reanalyses (RAOBCORE) Radiosonde Innovation Composite Homogenization (RICH) Version 1.9</td><td>https://webdata.wolke.img.univie.ac.at/haimberger/v1.9/</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) A2</td><td>https://www.ncei.noaa.gov/products/weather-balloon/radiosonde-atmospheric-temperature-products</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>Remote Sensing Systems (RSS) Version 4.0</td><td>https://www.remss.com/measurements/upper-air-temperature/</td></tr><tr><td>2b5</td><td>Temperature, Upper Atmosphere</td><td>University of Alabama in Huntsville (UAH) Microwave Sounding Unit (MSU) Version 6.1</td><td>https://www.nsstc.uah.edu/data/msu/v6.1/</td></tr><tr><td>2b5</td><td>Sea Surface Temperature</td><td>Niño 3.4 Index</td><td>https://psl.noaa.gov/data/timeseries/month/DS/Nino34_CPC/</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2b6</td><td>Temperature, Upper Atmosphere</td><td>Aura Microwave Limb Sounder (MLS)</td><td>https://discnrt1.gesdisc.eosdis.nasa.gov/data/Aura_MLS_NRT/ML2T_NRT.005/</td></tr><tr><td>2b7</td><td>Equivalent Temperature, [Near] Surface</td><td>Met Office Hadley Centre International Surface Dataset of Humidity Over Land (HadISDH. land).4.6.1.2024f</td><td>https://www.metoffice.gov.uk/hadobs/hadisdh/</td></tr><tr><td>2b7</td><td>Equivalent Temperature, [Near] Surface</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr></tbody></table>

## Section 2c Cryosphere

<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2c1</td><td>Permafrost</td><td>Global Terrestrial Network for Permafrost (GTN-P)</td><td>http://gtnpdatabase.org/</td></tr><tr><td>2c1</td><td>Permafrost</td><td>GTN-P Global Mean Annual Ground Temperature Data for Permafrost</td><td>https://doi.org/10.1594/PANGAEA.884711</td></tr><tr><td>2c1</td><td>Permafrost</td><td>Permafrost Temperature at Chinese Qinghai-Tibet Plateau (QTP) Sites</td><td>https://nsidc.org/data/GGD700/versions/1</td></tr><tr><td>2c1</td><td>Permafrost</td><td>Permafrost Temperature in European Mountains</td><td>https://zenodo.org/records/13628540</td></tr><tr><td>2c1</td><td>Permafrost</td><td>Permafrost Temperature at French Sites</td><td>https://permafrance.osug.fr</td></tr><tr><td>2c1</td><td>Permafrost</td><td>Permafrost Temperature at Norwegian Sites</td><td>https://cryo.met.no/</td></tr><tr><td>2c1, 2c2</td><td>Permafrost</td><td>Permafrost Temperature at Swiss Sites (Swiss Permafrost Monitoring Network [PERMOS])</td><td>https://www.permos.ch;<br>https://www.permos.ch/doi/permos-dataset-2022-1</td></tr><tr><td>2c1</td><td>Active Layer Depth</td><td>Circumpolar Active Layer Monitoring (CALM)</td><td>https://www.gwu.edu/~calm/</td></tr><tr><td>2c2</td><td>Rock Glacier Velocity</td><td>Regional Rock Glacier Velocity</td><td>Available from authors upon request. Austria: V. Kaufmann and A. Kellerer-Pirklbauer, Central Asia: A. Kääb, Dry Andes: S. Vivero, France: X. Bodin, D. Cusicanqui and E. Thibert, Switzerland: R. Delaloye, J. Noetzli and C. Pellet</td></tr><tr><td>2c3</td><td>Glacier Mass, Area or Volume</td><td>World Glacier Monitoring Service</td><td>https://dx.doi.org/10.5904/wgms-fog-2022-09</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr></tbody></table>
<table><thead><tr><td>Sub-section</td><td>General Variable or Phenomenon</td><td>Specific Dataset or Variable</td><td>Source</td></tr></thead><tbody><tr><td>2c4</td><td>Lake Ice</td><td>Lake Ice Clearance and Formation Data for Green Lakes Valley, 1968—Ongoing. Version 6. Environmental Data Initiative</td><td>https://doi.org/10.6073/pasta/e89a9a6984ebbcbd85c16d65298dd2</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>Global Lake and River Ice Phenology Database, Version 1</td><td>https://doi.org/10.7265/N5W66HP8</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>Mountain Lake Biology, Chemistry, Physics, and Climate Data Since 1959 at Castle Lake Version 1. Environmental Data Initiative</td><td>https://doi.org/10.6073/pasta/a8e3b81cfe5864731b29ad42506c65d7</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>Great Lakes Annual Maximum Ice Cover (%)</td><td>https://www.glerl.noaa.gov/data/ice/</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>Great Lakes Ice</td><td>https://www.glerl.noaa.gov/data/ice</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>Geographic Variation and Temporal Trends in Ice Phenology in Norwegian Lakes During a Century, Dryad</td><td>https://datadryad.org/stash/dataset/doi:10.5061/dryad.bk3j9kd9x</td></tr><tr><td>2c4</td><td>Lake Ice</td><td>Lake Surface Water Temperature and Ice Cover in Subalpine Lake Lunz, Austria</td><td>https://doi.org/10.1080/20442041.2017.1294332</td></tr><tr><td>2c4</td><td>Temperature, [Near] Surface</td><td>NASA/Goddard Institute for Space Studies (GISS) Global Temperature</td><td>https://data.giss.nasa.gov/gistemp/</td></tr><tr><td>2c5</td><td>Snow Properties</td><td>Northern Hemisphere (NH) Snow Cover Extent (SCE) Version 1</td><td>https://doi.org/10.7289/V5N014G9; https://www.snowcover.org</td></tr></tbody></table>
Section 2d Hydrological cycle

<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2d1</td><td>Humidity, [Near] Surface</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2d1, 2d2</td><td>Humidity, [Near] Surface</td><td>Met Office Hadley Centre Integrated Surface Dataset of Humidity over Land (HadISDH.land.4.6.1.2024f), Met Office HadISDH over Ocean (HadISDH.marine.1.6.1.2024f), Met Office HadISDH over Land and Ocean (HadISDH.blend.1.5.1.2024f), Met Office HadISDH of Humidity Extremes (HadISDH.extremes.1.2.0.2024f)</td><td>https://www.metoffice.gov.uk/hadobs/hadisdh</td></tr><tr><td>2d1</td><td>Humidity, [Near] Surface</td><td>Japanese Reanalysis for Three Quarters of a Century (JRA-3Q)</td><td>https://jra.kishou.go.jp/JRA-3Q/index_en.html</td></tr><tr><td>2d1</td><td>Humidity, [Near] Surface</td><td>MERRA-2</td><td>https://disc.gsfc.nasa.gov/datasets/M2T1NXSLV_5.12.4/summary</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC)</td><td>https://cdaac-www.cosmic.ucar.edu/</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>Global Navigation Satellite System (GNSS) Ground-Based Total Column Water Vapor</td><td>https://doi.org/10.25326/68</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>JRA-3Q</td><td>https://jra.kishou.go.jp/JRA-3Q/index_en.html</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>MERRA-2</td><td>https://gmao.gsfc.nasa.gov/gmao-products/merra-2/</td></tr><tr><td>2d3</td><td>Water Vapor, Total Column</td><td>Special Sensor Microwave Imager (SSM/I)—Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) Ocean Total Column Water Vapor</td><td>https://www.remss.com</td></tr><tr><td>2d4</td><td>Humidity, Upper Atmosphere</td><td>Upper-Troposphere Humidity (UTH)</td><td>Available on request to Brian Soden (bsoden@miami.edu)</td></tr><tr><td>2d4</td><td>Humidity, Upper Atmosphere</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2d4</td><td>Humidity, Upper Atmosphere</td><td>High Resolution Infrared Sounder (HIRS)</td><td>https://www.ncei.noaa.gov/products/climate-data-records/hirs-ch12-brightness-temperature</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2d4</td><td>Temperature, Upper Atmosphere</td><td>NOAA National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Applications and Research (STAR) Microwave Sounding Unit (MSU) Version 5</td><td>https://www.star.nesdis.noaa.gov/pub/smcd/emb/mscat/data/MSU_AMSU_v5.0/Monthly_Atmospheric_Layer_Mean_Temperature/</td></tr><tr><td>2d5, 2d6</td><td>Precipitation</td><td>Global Precipitation Climatology Centre (GPCC)</td><td>https://www.dwd.de/EN/ourservices/gpcc/gpcc.html</td></tr><tr><td>2d5</td><td>Precipitation</td><td>GPCPv2.3</td><td>https://www.ncei.noaa.gov/products/global-precipitation-climatology-project; https://rda.ucar.edu/datasets/d728008/dataaccess/</td></tr><tr><td>2d6</td><td>Precipitation</td><td>Multi-Source Weighted-Ensemble Precipitation (MSWEP)</td><td>https://www.gloh2o.org/mswep/</td></tr><tr><td>2d6</td><td>Precipitation</td><td>Met Office Hadley Centre Dataset of Extreme Indices (HadEX3) Version 3</td><td>https://www.metoffice.gov.uk/hadobs/hadex3/</td></tr><tr><td>2d6</td><td>Precipitation</td><td>ERA5</td><td>https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels</td></tr><tr><td>2d6</td><td>Precipitation</td><td>Global Historical Climatology Network Daily Extremes (GHCNDEX)</td><td>https://www.climdex.org</td></tr><tr><td>2d6</td><td>Precipitation</td><td>MERRA-2</td><td>https://gmao.gsfc.nasa.gov/gmao-products/merra-2/ https://disc.gsfc.nasa.gov/datasets/M2SMNXEDI_2/summary</td></tr><tr><td>2d7</td><td>Cloud properties</td><td>PATMOS-xv6.0</td><td>https://www.ncei.noaa.gov/products/climate-data-records/avhrr-hirs-cloud-properties-patmos</td></tr><tr><td>2d7</td><td>Cloud Properties</td><td>Clouds and the Earth's Radiant Energy System Energy Balance and Filled (CERES EBAF) Version 4.2</td><td>https://ceres.larc.nasa.gov/data/</td></tr><tr><td>2d8</td><td>Lake Water Storage and Level</td><td>'GloLakes' Lake and Reservoir Storage</td><td>https://doi.org/10.5194/essd-16-201-2024</td></tr><tr><td>2d8</td><td>Lake Water Storage and Level</td><td>Global Lakes and Reservoir Monitor (GREALM) Lake Level</td><td>https://ipad.fas.usda.gov/cropexplorer/global_reservoir/</td></tr><tr><td>2d9</td><td>River Discharge</td><td>Global Flood Awareness System Version (GloFAS) 4</td><td>https://ewds.climate.copernicus.eu/datasets/cems-glofas-historical; https://data.jrc.ec.europa.eu/dataset/68050d73-9c06-499c-a441-dc5053cb0c86</td></tr><tr><td>2d9</td><td>River Discharge</td><td>ERA5</td><td>https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels</td></tr><tr><td>2d10</td><td>Groundwater and Terrestrial Water Storage</td><td>Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO)</td><td>https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI GRID_RL06.3_V4</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2d11</td><td>Soil Moisture</td><td>Copernicus Climate Change Service (C3S) Version 202012 product based on the ESA Climate Change Initiative for Soil Moisture (ESA CCI SM) Version 05.2 Merging Algorithm</td><td>https://cds.climate.copernicus.eu/datasets/satellite-soil-moisture</td></tr><tr><td>2d12</td><td>Drought</td><td>Climatic Research Unit Gridded Time Series (CRU TS) 4.09</td><td>https://crudata.uea.ac.uk/cru/data/drought/</td></tr><tr><td>2d13</td><td>Land Evaporation</td><td>Global Land Evaporation Amsterdam Model (GLEAM)</td><td>https://www.gleam.eu/</td></tr><tr><td>2d13</td><td>Modes of Variability</td><td>Niño 3.4 index</td><td>https://psl.noaa.gov/data/timeseries/month/DS/Nino34</td></tr></tbody></table>

## Section 2e Atmospheric circulation

<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2e1, 2e3</td><td>Modes of Variability</td><td>Antarctic Oscillation (AAO)/Southern Annular Mode (SAM)</td><td>https://ftp.cpc.ncep.noaa.gov/cwlinks/norm.daily.aao.index.b790101.current.utf8</td></tr><tr><td>2e1</td><td>Pressure, Sea Level or Near-Surface</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2e2</td><td>Wind, [Near] Surface</td><td>ERA5</td><td>https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5</td></tr><tr><td>2e2</td><td>Wind, [Near] Surface</td><td>Met Office Hadley Centre Integrated Surface Dataset (HadISD) Version v3.4.1.2024f</td><td>https://hadleyserver.metoffice.gov.uk/hadisd/v341_2024f/index.html</td></tr><tr><td>2e2</td><td>Wind, [Near] Surface</td><td>Global Historical Climate Network hourly (GHCNh)</td><td>https://www.ncei.noaa.gov/products/global-historical-climatology-network-hourly</td></tr><tr><td>2e2</td><td>Wind, [Near] Surface</td><td>MERRA-2</td><td>https://gmao.gsfc.nasa.gov/gmao-products/merra-2/</td></tr><tr><td>2e2</td><td>Wind, [Near] Surface</td><td>Remote Sensing System (RSS) Merged 1-Degree Monthly Radiometer Winds</td><td>https://www.remss.com/measurements/wind/</td></tr><tr><td>2e2</td><td>Wind, [Near] Surface</td><td>Remote Sensing System (RSS) Advanced Scatterometer (ASCAT)</td><td>https://www.remss.com/missions/ascat/</td></tr><tr><td>2e2</td><td>Wind, [Near] Surface</td><td>Remote Sensing System (RSS) QuickScat4</td><td>https://www.remss.com/missions/qscat/</td></tr><tr><td>2e3</td><td>Modes of Variability</td><td>Pacific Decadal Oscillation</td><td>https://psl.noaa.gov/data/timeseries/month/data/pdo.timeseries.sstens.data</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2e3</td><td>Wind, [Upper Atmosphere]</td><td>Quasi-biennial Oscillation (QBO)</td><td>https://www.atmohub.kit.edu/data/singapore2023.dat</td></tr><tr><td>2e3</td><td>Wind, [Upper Atmosphere]</td><td>ERA5 Hourly Data on Pressure Levels from 1940 to Present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)</td><td>https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels</td></tr><tr><td>2e3</td><td>Wind, [Upper Atmosphere]</td><td>MERRA-2</td><td>https://gmao.gsfc.nasa.gov/gmao-products/merra-2/</td></tr><tr><td>2e3</td><td>Wind, [Upper Atmosphere]</td><td>Japanese 55-Year Reanalysis (JRA-55) Atmospheric Reanalysis</td><td>https://jra.kishou.go.jp/JRA-55/index_en.html</td></tr><tr><td>2e3</td><td>Wind, [Upper Atmosphere]</td><td>Japanese Reanalysis for Three-Quarters of a Century (JRA-3Q)</td><td>https://jra.kishou.go.jp/JRA-3Q/index_en.html</td></tr><tr><td>2e4</td><td>Lightning</td><td>European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)</td><td>https://www.eumetsat.int/features/animations-europes-first-lightning-imager; https://user.eumetsat.int/news-events/news/mtg-lightning-imager-livel-2-data-available</td></tr></tbody></table>

Section 2f Earth's radiation budget

<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2f1</td><td>TOA Earth Radiation Budget</td><td>Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Version 4.2.1</td><td>https://ceres-tool.larc.nasa.gov/ord-tool/jsp/EBAFTOA421Selection.jsp</td></tr><tr><td>2f1</td><td>TOA Earth Radiation Budget</td><td>CERES Fast Longwave And Shortwave Radiative Fluxes (FLASHflux) Version 4C</td><td>https://ceres-tool.larc.nasa.gov/ord-tool/jsp/FLASH_TISASelection.jsp</td></tr><tr><td>2f1</td><td>TOA Earth Radiation Budget</td><td>Community-Consensus Total Solar Irradiance (TSI) Composit</td><td>https://spot.colorado.edu/~koppg/TSI/TSI_Composite-SIST.txt</td></tr><tr><td>2f2</td><td>Solar Transmission, Apparent</td><td>Mauna Loa Observatory</td><td>https://www.esrl.noaa.gov/gmd/webdata/grad/mloapt/mauna_loa_transmission.dat</td></tr><tr><td>2f2</td><td>Aerosol Optical Depth</td><td>NASA's Moderate Resolution Imaging Spectroradiometer (MODIS)</td><td>https://earthobservatory.nasa.gov/global-maps/MODAL2_M_AER OD</td></tr><tr><td>2f2</td><td>Aerosol Optical Depth</td><td>Copernicus Atmosphere Monitoring Service (CAMS)</td><td>https://atmosphere.copernicus.eu/south-america-sees-historic-emissions-during-2024-wildfire-season</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2f2</td><td>Aerosol Extinction Coefficient</td><td>Stratospheric Aerosol and Gas Experiment (SAGE) Limb Sounder</td><td>https://sage.nasa.gov/2024/09/sage-iii-iss-science-highlight/</td></tr><tr><td>2f2</td><td>Stratospheric Aerosol Loadings</td><td>Balloon Network for Stratospheric Aerosol Observations (BalNeO)</td><td>https://science.larc.nasa.gov/balneo/</td></tr><tr><td>2f2</td><td>Stratospheric Aerosol Loadings</td><td>Balloon Baseline Stratospheric Aerosol Profiles (B2SAP)</td><td>https://csl.noaa.gov/projects/b2sap/pops.php?loc=HIH</td></tr></tbody></table>

## Section 2g Atmospheric composition

<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2g1</td><td>Trace Gases</td><td>Atmospheric Gas Trends</td><td>https://gml.noaa.gov/ccgg/trends/</td></tr><tr><td>2g1</td><td>Trace Gases</td><td>Global Greenhouse Gas Reference Network</td><td>https://gml.noaa.gov/ccgg/about.html</td></tr><tr><td>2g1</td><td>Trace Gases</td><td>Atmospheric Greenhouse Gas Index (AGGI)</td><td>https://gml.noaa.gov/ccgg/trends/</td></tr><tr><td>2g2</td><td>Trace Gases</td><td>Halocarbons and Other Atmospheric Trace Species</td><td>https://gml.noaa.gov/hats/data.html</td></tr><tr><td>2g2</td><td>Trace Gases</td><td>Ozone-Depleting Gas Index (ODGI)</td><td>https://gml.noaa.gov/odgi/</td></tr><tr><td>2g3</td><td>Aerosols</td><td>Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA)</td><td>https://www.ecmwf.int/en/research/climate-reanalysis/cams-reanalysis</td></tr><tr><td>2g4</td><td>Ozone, Surface</td><td>NOAA Global Monitoring Laboratory</td><td>https://gml.noaa.gov/aftp/data/ozwv/SurfaceOzone/</td></tr><tr><td>2g4</td><td>Ozone, Tropospheric</td><td>Ozone Monitoring Instrument (OMI)/ Microwave Limb Sounder (MLS)</td><td>https://acdb-ext.gsfc.nasa.gov/Data_services/cloud slice/; https://avdc.gsfc.nasa.gov/</td></tr><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>Haute-Provence Observatory (OHP) Lidar Temperature Aerosol (LTA) Lidar</td><td>https://www-air.larc.nasa.gov/missions/ndacc/data.html?station=haute.provence/ames/lidar/</td></tr><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>Stratospheric Ozone Lidar at Haute-Provence Observatory (LiO3S)</td><td>https://www-air.larc.nasa.gov/missions/ndacc/data.html?station=haute.provence/ames/lidar/</td></tr><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>Lauder Aerosol Lidar</td><td>https://www-air.larc.nasa.gov/missions/ndacc/data.html?station=lauder/ames/lidar/</td></tr></tbody></table>
<table><thead><tr><td>Sub-section</td><td>General Variable or Phenomenon</td><td>Specific Dataset or Variable</td><td>Source</td></tr></thead><tbody><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>Stratospheric Aerosol and Gas Experiment III (SAGE III) Version 5.3</td><td>https://asdc.larc.nasa.gov/project/SAGE%20III-ISS/g3bssp_53</td></tr><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>Global Space-based Stratospheric Aerosol Climatology (GloSSAC) Version 2</td><td>https://asdc.larc.nasa.gov/project/GloSSAC</td></tr><tr><td>2g5</td><td>Stratospheric Aerosols</td><td>Ozone Mapping and Profiler Suite Limb Profiler (OMPS-LP) Version 2.1</td><td>https://disc.gsfc.nasa.gov/datasets/OMPS_NPP_LP_L2_AER_DAILY_2/summary</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>Global Ozone Monitoring Experiment (GOME)/ Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY)/GOME2 (GSG) Merged Total Ozone</td><td>https://www.iup.uni-bremen.de/UVSAT/data/wfdoas/</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>GOME/SCIAMACHY/GOME2 (GTO) Merged Total Ozone</td><td>https://atmos.eoc.dlr.de/app/products/gto-ecv</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>Global Ozone Chemistry and Related Trace Gas Data Records (GOZCARDS) Ozone Profiles</td><td>https://disc.gsfc.nasa.gov/datasets/GozMmlpO3_1/summary</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>Multi Sensor Reanalysis (MSR-2) of Total Ozone</td><td>https://www.temis.nl/protocols/O3global.html</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>NASA Backscatter Ultraviolet Radiometer (BUV)/Solar Backscatter Ultraviolet Radiometer (SBUV)/Ozone Mapping and Profiler Suite (OMPS) Version 8.7 (MOD) Merged Ozone</td><td>https://acd-ext.gsfc.nasa.gov/Data_services/merged/</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>NOAA SBUV V8.6 OMPS V4r1 Cohesive Dataset (COH)</td><td>https://ftp.cpc.ncep.noaa.gov/SBUV_CDR/</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>Network for the Detection of Atmospheric Composition Change (NDACC) Lidar, Microwave, and Fourier Transform Infrared Spectroscopy (FTIR)</td><td>https://www-air.larc.nasa.gov/missions/ndacc</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>Chemistry-Climate Model Initiative (CCMI)-2022 Model Runs</td><td>https://blogs.reading.ac.uk/ccmi/ccmi-2022/</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>SAGE-Climate Change Initiative (CCI)-OMPS</td><td>https://climate.esa.int/en/projects/ozone/data</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>SAGE/Origins, Spectral Interpretation, Resource Identification, and Security (OSIRIS)</td><td>https://doi.org/10.5194/amt-11-489-2018<br>https://research-groups.usask.ca/osiris/</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>SAGE-SCIA-OMPS</td><td>https://doi.org/10.5194/amt-2018-275<br>https://www.iup.uni-bremen.de/DataRequest/</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>Stratospheric Water and Ozone Satellite Homogenized (SWOOSH)</td><td>https://csl.noaa.gov/groups/csl8/swoosh/</td></tr><tr><td>2g6</td><td>Ozone, Total Column and Stratospheric</td><td>World Ozone and Ultraviolet Radiation Data Centre (WOUDC) Ground-Based Ozone</td><td>https://woudc.org/data/dataset_info.php?id=totalozone</td></tr><tr><td>2g7</td><td>Stratospheric Water Vapor</td><td>The Aura Microwave Limb Sounder Version 5.0 Data, as Merged Into SWOOSH</td><td>https://csl.noaa.gov/groups/csl8/swoosh/</td></tr><tr><td>2g7</td><td>Tropopause Temperature</td><td>MERRA-2</td><td>https://gmao.gsfc.nasa.gov/gmao-products/merra-2/</td></tr><tr><td>2g7</td><td>Stratospheric Water Vapor</td><td>NOAA Frostpoint Hygrometer (FPH)</td><td>https://gml.noaa.gov/aftp/data/ozwv/WaterVapor/</td></tr><tr><td>2g7</td><td>Stratospheric Water Vapor</td><td>Cryogenic Frostpoint Hygrometer (CFH)</td><td>https://ndacc.org</td></tr><tr><td>2g8</td><td>Trace Gases</td><td>Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) for Carbon Monoxide</td><td>https://ads.atmosphere.copernicus.eu/datasets/cams-global-radiative-forcing-auxilliary-variables?tab=overview</td></tr></tbody></table>

## Section 2h Land surface properties

<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2h1</td><td>Albedo</td><td>Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra+Aqua Bidirectional Reflectance Distribution Function (BDRF)/Albedo Albedo Daily L3 Global 0.05 Deg Climate Modeling Grid (CMG) Version 061</td><td>https://www.earthdata.nasa.gov/centers/lp-daac</td></tr><tr><td>2h1</td><td>Albedo</td><td>VIIRS VNP43C3 Collection 1.0</td><td>https://www.earthdata.nasa.gov/centers/lp-daac</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2h2</td><td>Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</td><td>Joint Research Centre (JRC) Two-Stream Inversion Package (TIP) MODIS</td><td>https://fapar.jrc.ec.europa.eu</td></tr><tr><td>2h2</td><td>FAPAR</td><td>Medium Resolution Imaging Spectrometer (MERIS)</td><td>https://fapar.jrc.ec.europa.eu</td></tr><tr><td>2h2</td><td>FAPAR</td><td>Sea-Viewing Wide Field-of-View Sensor (SeaWIFS) FAPAR</td><td>https://fapar.jrc.ec.europa.eu/</td></tr><tr><td>2h2</td><td>FAPAR</td><td>Ocean and Land Colour Instrument (OLCI)</td><td>https://dataspace.copernicus.eu/</td></tr><tr><td>2h3</td><td>Biomass, Greenness or Burning</td><td>Global Fire Assimilation System Version (GFAS) 1.2</td><td>https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-fire-emissions-gfas v1.4 available upon request</td></tr><tr><td>2h3</td><td>Biomass, Greenness or Burning</td><td>Global Fire Emissions Database</td><td>https://www.globalfiredata.org/data.html</td></tr><tr><td>2h4</td><td>Phenology</td><td>MODIS Normalized Difference Vegetative Index (NDVI)</td><td>https://modis.gsfc.nasa.gov/data/dataprod/mod13.php</td></tr><tr><td>2h4</td><td>Temperature [Near] Surface</td><td>MERRIS-2 Monthly Temperature</td><td>https://goldsmr4.gesdisc.eosdis.nasa.gov/data/MERRA2_MONTHLY/ M2TMNXLND.5.12.4/</td></tr><tr><td>2h4</td><td>Phenology</td><td>USA-National Phenology Network (NPN) Phenology Data</td><td>https://www.usanpn.org/data/observational</td></tr><tr><td>2h4</td><td>Phenology</td><td>USA-National Phenology Network (NPN) Spring Index Raster Data Products</td><td>https://data.usanpn.org/geoserver-request-builder/</td></tr><tr><td>2h4</td><td>Phenology</td><td>German Oak Phenology Data</td><td>https://opendata.dwd.de/</td></tr><tr><td>2h4</td><td>Phenology</td><td>Harvard Forest</td><td>https://harvardforest1.fas.harvard.edu/exist/apps/datasets/showData.html?id=hf003</td></tr><tr><td>2h4</td><td>Phenology</td><td>Natures Calendar</td><td>https://naturescalendar.woodlandtrust.org.uk/</td></tr><tr><td>2h4</td><td>Phenology</td><td>PhenoCam</td><td>https://phenocam.sr.unh.edu</td></tr><tr><td>2h4</td><td>Phenology</td><td>UK Cumbrian Lakes Data</td><td>https://catalogue.ceh.ac.uk/documents/bf30d6aa-345a-4771-8417-ffbcf8c08c28/</td></tr><tr><td>2h4</td><td>Phenology</td><td>UK Loch Leven</td><td>https://catalogue.ceh.ac.uk/documents/ac973b0d-2c99-4e00-8931-22aa0881006d</td></tr><tr><td>2h4</td><td>Phenology</td><td>Dutch Oak Phenology Data</td><td>https://www.natuurkalender.nl/</td></tr><tr><td>2h4</td><td>Phenology</td><td>Lakes Geneva and Bourget Data</td><td>https://si-ola.inrae.fr/si_lacs/login.jsf</td></tr></tbody></table>
<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>2h5</td><td>Vegetation Optical Depth</td><td>Global Long-term Microwave Vegetation Optical Depth Climate Archive Version 2 (VODCAv2)</td><td>https://researchdata.tuwien.ac.at/records/t74ty-tcx62</td></tr><tr><td>2h5</td><td>Modes of Variability</td><td>Niño 3.4 Index</td><td>https://psl.noaa.gov/data/timeseries/month/DS/Nino34</td></tr></tbody></table>

## Sidebar 2.1 Super extreme land surface temperature hotspots

<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>SB2.1</td><td>Temperature [Near] Surface</td><td>Climatic Research Unit Temperature Version 5 (CRUTEM5)</td><td>https://www.metoffice.gov.uk/hadobs/crutem5/</td></tr><tr><td>SB2.1</td><td>Temperature [Near] Surface</td><td>Centre for Environmental Data Analysis (CEDA) Archive, European Space Agency (ESA) Land Surface Temperature Climate Change Initiative (LST_cci)—Sea and Land Surface Temperature Radiometer Onboard the Sentinel-3B Platform (SLSTR-B)</td><td>https://catalogue.ceda.ac.uk/uuid/5f66a881adf846bfaad58b0e6068f0ea/</td></tr><tr><td>SB2.1</td><td>Temperature [Near] Surface</td><td>CEDA Archive, Earth Observation Climate Information Service (EOCIS)—SLSTR-B</td><td>https://catalogue.ceda.ac.uk/uuid/fc0bc3d5887d441296091a8025f8f45d/</td></tr></tbody></table>

## Sidebar 2.2 Short-lived greenhouse gases

<table><thead><tr><th>Sub-section</th><th>General Variable or Phenomenon</th><th>Specific Dataset or Variable</th><th>Source</th></tr></thead><tbody><tr><td>SB2.2</td><td>Trace Gases [NH3]</td><td>Cross-track Infrared Sounder (CrIS) Column NH3</td><td>https://doi.org/10.5067/7I3KMUCCJNEN</td></tr><tr><td>SB2.2</td><td>Trace Gases [PAN]</td><td>CrIS Partial Column Acyl Peroxynitrates (PANs) Data</td><td>https://doi.org/10.5067/W0W6L8M6J85X</td></tr><tr><td>SB2.2</td><td>Total Column Ozone</td><td>Ozone Mapping and Profiler Suite (OMPS) Satellite Instrument Data</td><td>https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc%3AC01464/html</td></tr></tbody></table>
Appendix 3: Supplemental materials

<figure><img src="image_156.png" /><figcaption>Fig. A2.1. Global surface temperature anomalies (°C). (Source: NASA Goddard Institute for Space Studies Surface Temperature Analysis version 4 [GISTEMPv4].)</figcaption></figure>

<figure><img src="image_157.png" /><figcaption>Fig. A2.2. Global surface temperature anomalies (°C). (Source: HadCRUTS.)</figcaption></figure>
<figure><img src="image_158.png" /><figcaption>Fig. A2.3. Global surface temperature anomalies (°C). (Source: ERA5.)</figcaption></figure>

<figure><img src="image_159.png" /><figcaption>Fig. A2.4. Global surface temperature anomalies (°C). (Source: Japanese Reanalysis for Three Quarters of a Century [JRA-3Q].)</figcaption></figure>
<figure><img src="image_160.png" /><figcaption>(a) GHCNDEX TX90p - Warm Days</figcaption></figure>

Anomalies from 1961-1990 (days)

<figure><img src="image_161.png" /><figcaption>(b) GHCNDEX TX10p - Cool Nights</figcaption></figure>

Anomalies from 1961-1990 (days)

<figure><img src="image_162.png" /><figcaption>(c) GHCNDEX TXx - max Tmax</figcaption></figure>

Anomalies from 1961-1990 (°C)

Fig. A2.5. Global Historical Climatology Network Daily Extremes (GHCNDEX) (a) warm day threshold exceedance (TX90p), (b) cool night threshold exceedance (TN10p), and (c) annual highest daily maximum temperature (TXx).
<table><caption>Table A2.1. Ten highest annual global equivalent temperature anomalies (Teq) and their constituent parts (air temperature [Ta]; latent temperature [Tq]) in °C (1991–2020 base period) since 1979 for ERA5.</caption><thead><tr><th>Rank</th><th>Ta Year</th><th>Ta (°C)</th><th>Tq Year</th><th>Tq (°C)</th><th>Teq (Year)</th><th>Teq (°C)</th></tr></thead><tbody><tr><td>1</td><td>2024</td><td>0.72</td><td>2024</td><td>0.85</td><td>2024</td><td>1.57</td></tr><tr><td>2</td><td>2023</td><td>0.60</td><td>2023</td><td>0.59</td><td>2023</td><td>1.19</td></tr><tr><td>3</td><td>2016</td><td>0.44</td><td>2016</td><td>0.50</td><td>2016</td><td>0.94</td></tr><tr><td>4</td><td>2020</td><td>0.43</td><td>2019</td><td>0.44</td><td>2019</td><td>0.84</td></tr><tr><td>5</td><td>2019</td><td>0.40</td><td>2020</td><td>0.38</td><td>2020</td><td>0.82</td></tr><tr><td>6</td><td>2017</td><td>0.34</td><td>2017</td><td>0.33</td><td>2017</td><td>0.68</td></tr><tr><td>7</td><td>2022</td><td>0.30</td><td>1998</td><td>0.27</td><td>2015</td><td>0.53</td></tr><tr><td>8</td><td>2021</td><td>0.27</td><td>2015</td><td>0.27</td><td>2018</td><td>0.52</td></tr><tr><td>9</td><td>2018</td><td>0.26</td><td>2018</td><td>0.25</td><td>2021</td><td>0.41</td></tr><tr><td>10</td><td>2015</td><td>0.26</td><td>2010</td><td>0.15</td><td>2022</td><td>0.40</td></tr></tbody></table>

<table><caption>Table A2.2. Ten highest annual global equivalent temperature anomalies (Teq) and their constituent parts (air temperature [Ta]; latent temperature [Tq]) in °C (1991–2020 base period) since 1979 for the Japanese Reanalysis for Three Quarters of a Century (JRA-3Q).</caption><thead><tr><th>Rank</th><th>Ta Year</th><th>Ta (°C)</th><th>Tq Year</th><th>Tq (°C)</th><th>Teq (Year)</th><th>Teq (°C)</th></tr></thead><tbody><tr><td>1</td><td>2024</td><td>0.67</td><td>2024</td><td>0.98</td><td>2024</td><td>1.65</td></tr><tr><td>2</td><td>2023</td><td>0.57</td><td>2023</td><td>0.74</td><td>2023</td><td>1.30</td></tr><tr><td>3</td><td>2016</td><td>0.42</td><td>2016</td><td>0.45</td><td>2016</td><td>0.86</td></tr><tr><td>4</td><td>2020</td><td>0.38</td><td>2020</td><td>0.41</td><td>2020</td><td>0.79</td></tr><tr><td>5</td><td>2019</td><td>0.36</td><td>1998</td><td>0.33</td><td>2019</td><td>0.68</td></tr><tr><td>6</td><td>2017</td><td>0.30</td><td>2019</td><td>0.32</td><td>2017</td><td>0.52</td></tr><tr><td>7</td><td>2015</td><td>0.25</td><td>2015</td><td>0.24</td><td>2015</td><td>0.49</td></tr><tr><td>8</td><td>2022</td><td>0.24</td><td>2017</td><td>0.23</td><td>2022</td><td>0.44</td></tr><tr><td>9</td><td>2018</td><td>0.22</td><td>2022</td><td>0.21</td><td>2021</td><td>0.38</td></tr><tr><td>10</td><td>2021</td><td>0.19</td><td>2021</td><td>0.19</td><td>1998</td><td>0.36</td></tr></tbody></table>

<table><caption>Table A2.3. Top ten ranked global equivalent temperature anomalies (Teq) and their constituent parts (air temperature [Ta]; latent temperature [Tq]) in °C (1991–2020 base period) since 1979 for the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH; land-only).</caption><thead><tr><th>Rank</th><th>Ta Year</th><th>Ta (°C)</th><th>Tq Year</th><th>Tq (°C)</th><th>Teq (Year)</th><th>Teq (°C)</th></tr></thead><tbody><tr><td>1</td><td>2024</td><td>0.96</td><td>2024</td><td>1.13</td><td>2024</td><td>2.04</td></tr><tr><td>2</td><td>2023</td><td>0.74</td><td>2023</td><td>0.74</td><td>2023</td><td>1.49</td></tr><tr><td>3</td><td>2020</td><td>0.58</td><td>2016</td><td>0.61</td><td>2016</td><td>1.14</td></tr><tr><td>4</td><td>2016</td><td>0.55</td><td>1998</td><td>0.58</td><td>2020</td><td>1.03</td></tr><tr><td>5</td><td>2019</td><td>0.44</td><td>2020</td><td>0.46</td><td>1998</td><td>0.72</td></tr><tr><td>6</td><td>2015</td><td>0.42</td><td>2010</td><td>0.35</td><td>2017</td><td>0.67</td></tr><tr><td>7</td><td>2017</td><td>0.39</td><td>2022</td><td>0.31</td><td>2019</td><td>0.64</td></tr><tr><td>8</td><td>2021</td><td>0.37</td><td>2017</td><td>0.29</td><td>2021</td><td>0.63</td></tr><tr><td>9</td><td>2022</td><td>0.33</td><td>2021</td><td>0.27</td><td>2022</td><td>0.62</td></tr><tr><td>10</td><td>2018</td><td>0.24</td><td>2019</td><td>0.21</td><td>2015</td><td>0.60</td></tr></tbody></table>
<figure><img src="image_163.png" /><figcaption>Fig. A2.6. Surface specific humidity anomaly (g kg⁻¹) relative to 1991–2020 from Met Office Hadley Centre Integrated Surface Dataset of Humidity Blend (HadISDH.blend.1.5.1.2024f).</figcaption></figure>

<figure><img src="image_164.png" /><figcaption>Fig. A2.7. Surface relative humidity anomaly (%rh) relative to 1991–2020 from Met Office Hadley Centre Integrated Surface Dataset of Humidity over Land and Ocean (HadISDH.blend.1.5.1.2024f).</figcaption></figure>
<figure><img src="image_165.png" /><figcaption>Fig. A2.8. Surface specific humidity anomaly (g kg⁻¹) relative to 1991–2020 from MERRA-2.</figcaption></figure>

<figure><img src="image_166.png" /><figcaption>Fig. A2.9. Surface relative humidity anomaly (%rh) relative to 1991–2020 from MERRA-2.</figcaption></figure>
<figure><img src="image_167.png" /><figcaption>Fig. A2.10. 2024 minimum humid-heat intensity as measured by the annual median of the global median monthly minimum of the daily minimum wet-bulb temperature (T<sub>w</sub>N; °C) from the Met Office Hadley Centre Integrated Surface Dataset of Humidity Extremes (HadISDH/extremes). Gray gridboxes (over land) represent regions with insufficient data.</figcaption></figure>

<figure><img src="image_168.png" /><figcaption>Fig. A2.11. 2024 high daily minimum humid-heat frequency anomalies as measured by the number of days where the daily minimum wet-bulb temperature exceeds the local daily 90th percentile (T<sub>w</sub>N90p; days yr<sup>-1</sup>; calculated over the 1991-2020 period) from the Met Office Hadley Centre Integrated Surface Dataset of Humidity Extremes (HadISDH/extremes). Gray gridboxes (over land) represent regions with insufficient data.</figcaption></figure>
<figure><img src="image_169.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_170.png" /><figcaption>(c) CAMSRA: RFari (SW). Mean = -0.44±0.13 W m⁻²</figcaption></figure>

<figure><img src="image_171.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_172.png" /><figcaption>(e) CAMSRA: RFaci (SW). Mean = -0.54±0.41 W m⁻²</figcaption></figure>

<figure><img src="image_173.png" /><figcaption>(f)</figcaption></figure>

<figure><img src="image_174.png" /></figure>

Fig. A2.12. Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) (a) 2024 average of anthropogenic aerosol optical depth (AOD); (b) global annual average of anthropogenic AOD from 2003 to 2024. Radiative forcing in the short-wave (SW) spectrum due to (c),(d) aerosol-radiation (RFari) and (e),(f) aerosol-cloud interactions (RFaci). The left column shows the distributions for the year 2024. The right column shows the time series of global averages for the period 2003–24, with the mean ±1-σ uncertainties of these estimates shown in gray.
<figure><img src="image_175.png" /></figure>

Fig. A2.13. The difference in average CXKu vegetation optical depth (VOD) between the years 2023 and 2024. Brown (green) colors indicate areas where VOD in 2024 were lower (higher) than in 2023. (Source: Vegetation Optical Depth Climate Archive version 2 [VODCAv2]).

<figure><img src="image_176.png" /></figure>

Fig. A2.14. Vegetation Optical Depth Climate Archive (VODCA) monthly CXKu vegetation optical depth (VOD) anomalies in 2024 (1991–20 base period). VOD cannot be retrieved over frozen or snow-covered areas, which is why they are masked out in winter.
References

Alencar, A., and Coauthors, 2024: Fogo no Brasil em 2024: O retrato fundiário da área queimada nos biomas. Tech. Note IPAM (Amazon Environmental Research Institute), Map-Biomas Network, 21 pp., https://ipam.org.br/wp-content/uploads/2024/10/NT_Fogo_Fundiario_ENG_v01.pdf.

Allan, R. J., J. A. Lindesay, and D. E. Parker, 1996: *El Niño Southern Oscillation and Climatic Variability*. CSIRO Publishing, 405 pp.

Ammann, C. M., G. A. Meehl, W. M. Washington, and C. S. Zender, 2003: A monthly and latitudinally varying volcanic forcing dataset in simulations of 20th century climate. *Geophys. Res. Lett.*, **30**, 1657, https://doi.org/10.1029/2003GL016875.

Aono, Y., and K. Kazui, 2008: Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century. *Int. J. Climatol.*, **28**, 905–914, https://doi.org/10.1002/joc.1594.

Archibald, A. T., and Coauthors, 2020: Tropospheric ozone assessment report. *Elementa*, **8**, 034, https://doi.org/10.1525/elementa.2020.034.

Arguez, A., and Coauthors, 2020: Should we expect each year in the next decade (2019–28) to be ranked among the top 10 warmest years globally? *Bull. Amer. Meteor. Soc.*, **101**, E655–E663, https://doi.org/10.1175/BAMS-D-19-0215.1.

Arosio, C., A. Rozanov, E. Malinina, M. Weber, and J. P. Burrows, 2019: Merging of ozone profiles from SCIAMACHY, OMPS and SAGE II observations to study stratospheric ozone changes. *Atmos. Meas. Tech.*, **12**, 2423–2444, https://doi.org/10.5194/amt-12-2423-2019.

Asher, E., and Coauthors, 2024: Balloon baseline stratospheric aerosol profiles (B$^2$SAP)—Perturbations in the Southern Hemisphere, 2019–2022. *J. Geophys. Res. Atmos.*, **129**, e2024JD041581, https://doi.org/10.1029/2024JD041581.

Augustine, J. A., K. O. Lantz, J.-P. Vernier, and L. Soldo, 2024: Mauna Loa apparent transmission record update for 2023 [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S1–S484, https://doi.org/10.1175/2024BAMSStateoftheClimate.1.

Azisov, E., M. Hoelzle, S. Vorogushyn, T. Saks, R. Usubaliev, M. Esenaman Uulu, and M. Barandun, 2022: Reconstructed centennial mass balance change for Golubin glacier, northern Tien Shan. *Atmosphere*, **13**, 954, https://doi.org/10.3390/atmos13060954.

Azorin-Molina, C., and Coauthors, 2023: Biases in wind speed measurements due to anemometer changes. *Atmos. Res.*, **289**, 106771, https://doi.org/10.1016/j.atmosres.2023.106771.

—, R. J. H. Dunn, L. Ricciardulli, C. A. Mears, J. P. Nicolas, T. R. McVicar, Z. Zeng, and M. G. Bosilovich, 2024: Land and ocean surface winds [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S76–S79, https://doi.org/10.1175/BAMS-D-24-0116.1.

Bai, G., and Coauthors, 2022: GBOV (Ground-Based Observation for Validation): A copernicus service for validation of land products. *IGARSS 2022 – 2022 IEEE Int. Geoscience and Remote Sensing Symp.*, Kuala Lumpur, Malaysia, Institute of Electrical and Electronics Engineers, 4304–4307, https://doi.org/10.1109/IGARSS46834.2022.9883162.

Bai, X., J. Wang, C. Sellinger, A. Clites, and R. Assel, 2012: Inter-annual variability of Great Lakes ice cover and its relationship to NAO and ENSO. *J. Geophys. Res.*, **117**, C03002, https://doi.org/10.1029/2010JC006932.

Baldwin, M. P., and T. J. Dunkerton, 2001: Stratospheric harbingers of anomalous weather regimes. *Science*, **294**, 581–584, https://doi.org/10.1126/science.1063315.

—, and Coauthors, 2001: The quasi-biennial oscillation. *Rev. Geophys.*, **39**, 179–299, https://doi.org/10.1029/1999RG000073.

Barichivich, J., E. Gloor, P. Peylin, R. J. Brienen, J. Schöngart, J. C. Espinoza, and K. C. Pattnayak, 2018: Recent intensification of Amazon flooding extremes driven by strengthened Walker circulation. *Sci. Adv.*, **4**, eaat8785, https://doi.org/10.1126/sciadv.aat8785.

—, T. J. Osborn, I. Harris, G. van der Schrier, and P. D. Jones, 2024: Monitoring global drought using the self-calibrating palmer drought severity index [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S70–S71, https://doi.org/10.1175/2024BAMSStateoftheClimate.1.

Basu, S., and Coauthors, 2022: Estimating emissions of methane consistent with atmospheric measurements of methane and $\delta^{13}$C of methane. *Atmos. Chem. Phys.*, **22**, 15351–15377, https://doi.org/10.5194/acp-22-15351-2022.

Beck, H. E., E. F. Wood, M. Pan, C. K. Fisher, D. G. Miralles, A. I. J. M. Van Dijk, T. R. McVicar, and R. F. Adler, 2019: MSWEP V2 global 3-hourly 0.1° precipitation: Methodology and quantitative assessment. *Bull. Amer. Meteor. Soc.*, **100**, 473–500, https://doi.org/10.1175/BAMS-D-17-0138.1.

Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. A. Ziese, 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present. *Earth Syst. Data*, **5**, 71–99, https://doi.org/10.5194/essd-5-71-2013.

Behera, S. N., M. Sharma, V. P. Aneja, and R. Balasubramanian, 2013: Ammonia in the atmosphere: A review on emission sources, atmospheric chemistry and deposition on terrestrial bodies. *Environ. Sci. Pollut. Res.*, **20**, 8092–8131, https://doi.org/10.1007/s11356-013-2051-9.

Bell, B., and Coauthors, 2021: The ERA5 global reanalysis: Preliminary extension to 1950. *Quart. J. Roy. Meteor. Soc.*, **147**, 4186–4227, https://doi.org/10.1002/qj.4174.

Bellouin, N., and Coauthors, 2020: Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition. *Earth Syst. Sci. Data*, **12**, 1649–1677, https://doi.org/10.5194/essd-12-1649-2020.

Bessenbacher, V., D. L. Schumacher, M. Hirschi, S. I. Seneviratne, and L. Gudmundsson, 2023: Gap-filled multivariate observations of global land–climate interactions. *J. Geophys. Res. Atmos.*, **128**, e2023JD039099, https://doi.org/10.1029/2023JD039099.

Betts, R. A., C. D. Jones, J. R. Knight, R. F. Keeling, and J. J. Kennedy, 2016: El Niño and a record $CO_2$ rise. *Nat. Climate Change*, **6**, 806–810, https://doi.org/10.1038/nclimate3063.

Birkett, C., C. Reynolds, B. Beckley, and B. Doorn, 2011: From research to operations: The USDA global reservoir and lake monitor. Coastal Altimetry, S. Vignudelli et al., Eds., Springer, 19–50.

Bock, O., 2025: Global GNSS integrated water vapour data, 1994–2024. AERIS, accessed 15 July 2025, https://doi.org/10.25326/68.

—, C. Mears, S. P. Ho, and S. Xi, 2024: Total column water vapor [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S57–S59, https://doi.org/10.1175/BAMS-D-24-0116.1.
Bodhaine, B. A., B. G. Mendonca, J. M. Harris, and J. M. Miller, 1981: Seasonal variations in aerosols and atmospheric transmission at Mauna Loa observatory. *J. Geophys. Res.*, **86**, 7395–7398, https://doi.org/10.1029/JC086iC08p07395.

Boone, C. D., P. F. Bernath, and M. D. Fromm, 2020: Pyrocumulusstratospheric plume injections measured by the ACE-FTS. *Geophys. Res. Lett.*, **47**, e2020GL088442, https://doi.org/10.1029/2020GL088442.

Bowman, K. W., 2021a: TROPESS CrIS-JPSS1 L2 ammonia for forward stream, standard product V1. GES DISC, accessed 31 January 2025, https://doi.org/10.5067/7I3KMUCCJNEN.

—, 2021b: TROPESS CrIS-JPSS1 L2 peroxyacetyl nitrate for forward stream, standard product V. GES DISC, accessed 31 January 2025, https://doi.org/10.5067/W0W6L8M6J85X.

Brohan, P., J. J. Kennedy, I. Harris, S. F. Tett, and P. D. Jones, 2006: Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850. *J. Geophys. Res.*, **111**, D12106, https://doi.org/10.1029/2005JD006548.

Brutsaert, W., 2017: Global land surface evaporation trend during the past half century: Corroboration by Clausius-Clapeyron scaling. *Adv. Water Resour.*, **106**, 3–5, https://doi.org/10.1016/j.adwwatres.2016.08.014.

Burek, P., J. van der Knijff, and A. de Roo, 2013: LISFLOOD, distributed water balance and flood simulation model – Revised user manual 2013. Publications Office, 139 pp., https://doi.org/10.2788/24719.

Butchart, N., 2014: The Brewer-Dobson circulation. *Rev. Geophys.*, **52**, 157–184, https://doi.org/10.1002/2013RG000448.

Byrne, B., and Coauthors, 2024: Carbon emissions from the 2023 Canadian wildfires. *Nature*, **633**, 835–839, https://doi.org/10.1038/s41586-024-07878-z.

Caine, T. N. (Nel), J. F. Morse, and Niwot Ridge LTER, 2024: Lake ice clearance and formation data for Green Lakes Valley, 1968—Ongoing. Environmental Data Initiative, https://doi.org/10.6073/PASTA/947534B780396B2E79D2206C98640262.

Campos, D., and R. Rondanelli, 2023: ENSO-related precipitation variability in Central Chile: The role of large scale moisture transport. *J. Geophys. Res. Atmos.*, **128**, e2023JD038671, https://doi.org/10.1029/2023JD038671.

CAMS, 2024: CAMS Global wildfires review 2024: A harsh year for the Americas. https://atmosphere.copernicus.eu/cams-global-wildfires-review-2024-harsh-year-americas.

Capotondi, A., and Coauthors, 2015: Understanding ENSO diversity. *Bull. Amer. Meteor. Soc.*, **96**, 921–938, https://doi.org/10.1175/BAMS-D-13-00117.1.

Cappucci, F., N. Gobron, and G. Duveiller, 2024: Land surface properties: Terrestrial surface albedo dynamics [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S12–S155, https://doi.org/10.1175/BAMS-D-24-0116.1.

Carrea, L., O. Embury, and C. J. Merchant, 2015: Datasets related to in-land water for limnology and remote sensing applications: Distance-to-land, distance-to-water, water-body identifier and lake-Centre co-ordinates. *Geosci. Data J.*, **2**, 83–97, https://doi.org/10.1002/gdj3.32.

—, and Coauthors, 2019: Lake surface temperature [in "State of the Climate in 2018"). *Bull. Amer. Meteor. Soc.*, **100** (8), S13–S14, https://doi.org/10.1175/2019BAMSStateoftheClimate.1.

—, and Coauthors, 2020: Lake surface temperature [in "State of the Climate in 2019"). *Bull. Amer. Meteor. Soc.*, **101** (8), S26–S28, https://doi.org/10.1175/BAMS-D-20-0104.1.

—, and Coauthors, 2021: Lake surface water temperature [in "State of the Climate in 2020"). *Bull. Amer. Meteor. Soc.*, **102** (8), S28–S31, https://doi.org/10.1175/BAMS-D-21-0098.1.

—, and Coauthors, 2022a: Lake surface water temperature [in "State of the Climate in 2021"). *Bull. Amer. Meteor. Soc.*, **103** (8), S28–S30, https://doi.org/10.1175/BAMS-D-22-0092.1.

—, and Coauthors, 2022b: ESA Lakes climate change initiative (Lakes_cci): Lake products, version 2.0.2. NERC EDS Centre for Environmental Data Analysis, accessed 6 July 2022, https://doi.org/10.5285/a07deacaffb8453e93d57ee214676304.

—, and Coauthors, 2023a: Satellite-derived multivariate worldwide lake physical variable timeseries for climate studies. *Sci. Data*, **10**, 30, https://doi.org/10.1038/s41597-022-01889-z.

—, and Coauthors, 2023b: Lake surface temperature [in "State of the Climate in 2022"). *Bull. Amer. Meteor. Soc.*, **104** (9), S1–S516, https://doi.org/10.1175/2023BAMSStateoftheClimate.1.

—, and Coauthors, 2024: Lake surface temperature [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S1–S484, https://doi.org/10.1175/2024BAMSStateoftheClimate.1.

Cassidy, E., 2024: Typhoons line up in the western Pacific. NASA Earth Observatory, https://earthobservatory.nasa.gov/imag-es/153575/typhoons-line-up-in-the-western-pacific.

Cattiaux, J., A. Ribes, and E. Cariou, 2024: How extreme were daily global temperatures in 2023 and early 2024? *Geophys. Res. Lett.*, **51**, e2024GL110531, https://doi.org/10.1029/2024GL110531.

Chandra, S., E. K. Suenaga, F. Scordo, and T. A. Daniel, 2023: Mountain Lake Chemistry and physics profile data since 2015 at Castle Lake. Environmental Data Initiative, accessed 5 February 2025, https://doi.org/10.6073/PASTA/72811937CB436A198EC9B10483EDC2A4.

Chang, K. L., M. G. Schultz, X. Lan, A. McClure-Begley, I. Petropavlovskikh, X. Xu, and J. R. Ziemke, 2021: Trend detection of atmospheric time series: Incorporating appropriate uncertainty estimates and handling extreme events. *Elementa*, **9**, 00035, https://doi.org/10.1525/elementa.2021.00035.

—, and Coauthors, 2023: Diverging ozone trends above western North America: Boundary layer decreases versus free tropospheric increases. *J. Geophys. Res. Atmos.*, **128**, e2022JD038090, https://doi.org/10.1029/2022JD038090.

—, O. R. Cooper, A. Gaudel, I. Petropavlovskikh, P. Effertz, G. Morris, and B. C. McDonald, 2024: Technical note: Challenges in detecting free tropospheric ozone trends in a sparsely sampled environment. *Atmos. Chem. Phys.*, **24**, 6197–6218, https://doi.org/10.5194/acp-24-6197-2024.

Cheng, L., and Coauthors, 2025: Record high temperatures in the ocean in 2024. *Adv. Atmos. Sci.*, **42**, 1092–1109, https://doi.org/10.1007/s00376-025-4541-3.

Choulga, M., F. Moschini, C. Mazzetti, S. Grimaldi, J. Disperati, H. Beck, P. Salamon, and C. Prudhomme, 2024: Technical note: Surface fields for global environmental modelling. *Hydrol. Earth Syst. Sci.*, **28**, 2991–3036, https://doi.org/10.5194/hess-28-2991-2024.

Christy, J. R., 2017: Lower and mid-tropospheric temperature [in "State of the Climate in 2016"). *Bull. Amer. Meteor. Soc.*, **98** (8), S16–S17, https://doi.org/10.1175/2017BAMSStateoftheClimate.1.
Chung, E., B. Soden, and V. O. John, 2013: Intercalibrating micro-wave satellite observations for monitoring long-term variations in upper- and midtropospheric water vapor. *J. Atmos. Oceanic Technol.*, **30**, 2303–2319, https://doi.org/10.1175/JTECH-D-13-00001.1.

—, —, B. J. Sohn, and L. Shi, 2014: Upper-tropospheric moistening in response to anthropogenic warming. *Proc. Natl. Acad. Sci. USA*, **111** (32) 11636–11641, https://doi.org/10.1073/pnas.1409659111.

—, —, X. Huang, L. Shi, and V. O. John, 2016: An assessment of the consistency between satellite measurements of upper tropospheric water vapor. *J. Geophys. Res. Atmos.*, **121**, 2874–2887, https://doi.org/10.1002/2015JD024496.

Ciasto, L., and A. Butler, 2024: And that's a wrap on this season's stratospheric polar vortex. NOAA, https://www.climate.gov/news-features/blogs/polar-vortex/and-thats-wrap-seasons-stratospheric-polar-vortex.

Cicoira, A., J. Beutel, J. Faillettaz, and A. Vieli, 2019: Water controls the seasonal rhythm of rock glacier flow. *Earth Planet. Sci. Lett.*, **528**, 115844, https://doi.org/10.1016/j.epsl.2019.115844.

Climate.gov, 2024: Multi-year drought and heat waves across Mexico. NOAA, accessed 1 February 2025, https://www.climate.gov/news-features/event-tracker/multi-year-drought-and-heat-waves-across-mexico-2024.

Cooper, O. R., J. R. Ziemke, and K.-L. Chang, 2024a: Tropospheric ozone [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S94–S96, https://doi.org/10.1175/BAMS-D-24-0116.1.

—, and Coauthors, 2024b: Early season 2023 wildfires generated record-breaking surface ozone anomalies across the U.S. Upper Midwest. *Geophys. Res. Lett.*, **51**, e2024GL111481, https://doi.org/10.1029/2024GL111481.

Cornes, R. C., and R. Junod, 2024: Night marine air temperature [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S35–S37, https://doi.org/10.1175/2024BAMSStateoftheClimate.1.

—, E. C. Kent, D. I. Berry, and J. J. Kennedy, 2020: CLASSNmat: A global night marine air temperature data set, 1880–2019. *Geosci. Data J.*, **7**, 170–184, https://doi.org/10.1002/gdj3.100.

—, T. Cropper, R. Junod, and E. C. Kent, 2023: Night marine air temperature [in "State of the Climate in 2022"). *Bull. Amer. Meteor. Soc.*, **104** (9), S31–S32, https://doi.org/10.1175/2023BAMSStateoftheClimate.1.

Crimmins, T. M., R. L. Marsh, J. Switzer, M. A. Crimmins, K. L. Gerst, A. H. Rosemartin, and J. F. Weltzin, 2017: USA National Phenology Network gridded products documentation. USGS Open-File Rep. 2017-1003, 34 pp., https://doi.org/10.3133/ofr20171003.

—, E. G. Denny, E. E. Posthumus, A. H. Rosemartin, R. Croll, M. Montano, and H. Panci, 2022: Science and management advancements made possible by the USA National Phenology Network's nature's notebook platform. *BioScience*, **72**, 908–920, https://doi.org/10.1093/biosci/biac061.

Cusicanqui, D., P. Lacroix, X. Bodin, B. A. Robson, A. Kääb, and S. MacDonell, 2024: Detection and reconstruction of rock glaciers kinematic over 24 years (2000-2024) from Landsat imagery. EGUSphere, https://doi.org/10.5194/egusphere-2024-2393.

Daniel, J. S., S. Solomon, and D. L. Albritton, 1995: On the evaluation of halocarbon radiative forcing and global warming potentials. *J. Geophys. Res.*, **100**, 1271–1285, https://doi.org/10.1029/94JD02516.

Davidson, E. A., 2009: The contribution of manure and fertilizer nitrogen to atmospheric nitrous oxide since 1860. *Nat. Geosci.*, **2**, 659–662, https://doi.org/10.1038/ngeo608.

Davies, B., and Coauthors, 2024: Accelerating glacier volume loss on Juneau icefield driven by hypsometry and melt-accelerating feedbacks. *Nat. Commun.*, **15**, 5099, https://doi.org/10.1038/s41467-024-49269-y.

Davis, S. M., and Coauthors, 2016: The Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database: A long-term database for climate studies. *Earth Syst. Sci. Data*, **8**, 461–490, https://doi.org/10.5194/essd-8-461-2016.

—, K. Rosenlof, E. Asher, H. Vömel, R. Stauffer, and D. Hurst, 2024: Stratospheric water vapor [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S101–S103, https://doi.org/10.1175/BAMS-D-24-0116.1.

Deeter, M. N., and Coauthors, 2014: The MOPITT version 6 product: Algorithm enhancements and validation. *Atmos. Meas. Tech.*, **7**, 3623–3632, https://doi.org/10.5194/amt-7-3623-2014.

—, D. P. Edwards, G. L. Francis, J. C. Gille, S. Martínez-Alonso, H. M. Worden, and C. Sweeney, 2017: A climate-scale satellite record for carbon monoxide: The MOPITT version 7 product. *Atmos. Meas. Tech.*, **10**, 2533–2555, https://doi.org/10.5194/amt-10-2533-2017.

—, and Coauthors, 2019: Radiance-based retrieval bias mitigation for the MOPITT instrument: The version 8 product. *Atmos. Meas. Tech.*, **12**, 4561–4580, https://doi.org/10.5194/amt-12-4561-2019.

de Laat, A. T. J., N. Andela, M. Forkel, V. Huijnen, D. Kinalzcyzk, and D. van Wees, 2025: Sentinel-5p reveals unexplained large wildfire carbon emissions in the Amazon in 2024. ESS Open Archive, https://doi.org/10.22541/essoar.173869416.69575859/v1.

de Pablo, M. A., M. Ramos, G. Vieira, A. Molina, R. Ramos, C. N. Maior, M. Prieto, and J. Ruiz-Fernández, 2024: Interannual variability of ground surface thermal regimes in Livingston and deception islands, Antarctica (2007–2021). Land Degrad. Dev., **35**, 378–393, https://doi.org/10.1002/ldr.4922.

Dijk, V., and Coauthors, 2025: Global Water Monitor 2024 summary report. Global Water Monitor Consortium, 57 pp., https://www.globalwater.online/globalwater/report/#gallery.

Dlugokencky, E. J., L. P. Steele, P. M. Lang, and K. A. Masarie, 1994: The growth rate and distribution of atmospheric methane. *J. Geophys. Res.*, **99**, 17021–17043, https://doi.org/10.1029/94JD01245.

Dodangodage, R., P. F. Bernath, M. Wyatt, and C. Boone, 2025: Atmospheric chemistry experiment (ACE) satellite observations of aerosols and SO₂ emissions from the 2024 Ruang volcanic eruption. *J. Quant. Spectrosc. Radiat. Transfer*, **333**, 109333, https://doi.org/10.1016/j.jqsrt.2024.109333.

Domeisen, D. I. V., C. I. Garfinkel, and A. H. Butler, 2019: The teleconnection of El Niño Southern Oscillation to the stratosphere. *Rev. Geophys.*, **57**, 5–47, https://doi.org/10.1029/2018RG000596.

Donat, M. G., L. V. Alexander, H. Yang, I. Durre, R. Vose, and J. Caesar, 2013: Global Land-Based datasets for monitoring climatic extremes. *Bull. Amer. Meteor. Soc.*, **94**, 997–1006, https://doi.org/10.1175/BAMS-D-12-00109.1.

Dorigo, W., W. Preimesberger, C. Reimer, R. Van der Schalie, A. Pasik, R. De Jeu, and C. Paulik, 2025: Soil moisture gridded data from 1978 to present, v202312.0.0. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), accessed 10 February 2025, https://doi.org/10.24381/cds.d7782f18.
Dudok de Wit, T., G. Kopp, C. Fröhlich, and M. Schöll, 2017: Methodology to create a new total solar irradiance record: Making a composite out of multiple data records. *Geophys. Res. Lett.*, **44**, 1196–1203, https://doi.org/10.1002/2016GL071866.

Duncan, B. N., L. N. Lamsal, A. M. Thompson, Y. Yoshida, Z. Lu, D. G. Streets, M. M. Hurwitz, and K. E. Pickering, 2016: A space-based, high-resolution view of notable changes in urban NOₓ pollution around the world (2005–2014). *J. Geophys. Res. At-mos.*, **121**, 976–996, https://doi.org/10.1002/2015JD024121.

Dunn, R. J., 2019: HadISD version 3: Monthly updates. Hadley Centre Tech. Note 103, 10 pp., https://www.metoffice.gov.uk/research/library-and-archive/publications/science/climate-science-technical-notes.

—, K. M. Willett, P. W. Thorne, E. V. Woolley, I. Durre, A. Dai, D. E. Parker, and R. S. Vose, 2012: HadISD: A quality-controlled global synoptic report database for selected variables at long-term stations from 1973–2011. *Climate Past*, **8**, 1649–1679, https://doi.org/10.5194/cp-8-1649-2012.

—, —, D. E. Parker, and L. Mitchell, 2016: Expanding HadISD: Quality-controlled, sub-daily station data from 1931. *Geosci. Instrum. Method Data Syst.*, **5**, 473–491, https://doi.org/10.5194/gi-5-473-2016.

—, C. Azorin-Molina, M. J. Menne, Z. Zeng, N. W. Casey, and C. Shen, 2022a: Reduction in reversal of global stilling arising from correction to encoding of calm periods. *Environ. Res. Commun.*, **4**, 061003, https://doi.org/10.1088/2515-7620/ac770a.

—, M. G. Donat, and L. V. Alexander, 2022b: Comparing extremes indices in recent observational and reanalysis products. *Front. Climate*, **4**, 989505, https://doi.org/10.3389/fclim.2022.989505.

Dunn, R. J. H., and C. P. Morice, 2022: On the effect of reference periods on trends in percentile-based extreme temperature indices. *Environ. Res. Lett.*, **17**, 034026, https://doi.org/10.1088/1748-9326/ac52c8.

—, and Coauthors, 2020: Development of an updated global land in situ-based data set of temperature and precipitation extremes: HadEX3. *J. Geophys. Res. Atmos.*, **125**, e2019JD032263, https://doi.org/10.1029/2019JD032263.

—, M. G. Donat, R. W. Schlegel, and M. G. Bosilovich, 2024a: Surface temperature extremes [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S37–S39, https://doi.org/10.1175/BAMS-D-24-0116.1.

—, and Coauthors, 2024b: Observed global changes in sector-relevant climate extremes indices—An extension to HadEX3. *Earth Space Sci.*, **11**, e2023EA003279, https://doi.org/10.1029/2023EA003279.

Duveiller, G., and N. Gobron, 2023: Land surface properties: Terrestrial surface albedo dynamics [in "State of the Climate in 2022"). *Bull. Amer. Meteor. Soc.*, **104** (9), S102–S103, https://doi.org/10.1175/BAMS-D-23-0090.1.

DW, 2024: Pakistan: Lahore records heaviest rainfall in years. Accessed 13 February 2025, https://www.dw.com/en/pakistan-lahore-records-heaviest-rainfall-in-years/a-69829897.

ECMWF, 2025: Global Climate Highlights 2024. Climate Copernicus, accessed 13 February 2025, https://climate.copernicus.eu/global-climate-highlights-2024.

Edwards, M., and A. Richardson, 2004: Impact of climate change on marine pelagic phenology and trophic mismatch. *Nature*, **430**, 881–884, https://doi.org/10.1038/nature02808.

Ellis, H. T., and R. F. Pueschel, 1971: Solar radiation: Absence of air pollution trends at Mauna Loa. *Science*, **172**, 845–846, https://doi.org/10.1126/science.172.3985.845.

Elshorbany, Y., and Coauthors, 2024: Tropospheric ozone precursors: Global and regional distributions, trends, and variability. *Atmos. Chem. Phys.*, **24**, 12 225–12 257, https://doi.org/10.5194/acp-24-12225-2024.

Elson, P., and Coauthors, 2022: SciTools/cartopy: v0.21.1 (v0.21.1). Zenodo, https://doi.org/10.5281/zenodo.7430317.

Estilow, T. W., A. H. Young, and D. A. Robinson, 2015: A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring. *Earth Syst. Sci. Data*, **7**, 137–142, https://doi.org/10.5194/essd-7-137-2015.

Etheridge, D. M., L. P. Steele, R. L. Langenfelds, R. J. Francey, J. M. Barnola, and V. I. Morgan, 1996: Natural and anthropogenic changes in atmospheric CO₂ over the last 1000 years from air in Antarctic ice and firn. *J. Geophys. Res.*, **101**, 4115–4128, https://doi.org/10.1029/95JD03410.

Etzmüller, B., and Coauthors, 2020: Twenty years of European Mountain permafrost dynamics—The PACE legacy. *Environ. Res. Lett.*, **15**, 104070, https://doi.org/10.1088/1748-9326/abae9d.

—, K. Isaksen, J. Czekirda, S. Westermann, C. Hilbich, and C. Hauck, 2023: Rapid warming and degradation of Mountain permafrost in Norway and Iceland. *Cryosphere*, **17**, 5477–5497, https://doi.org/10.5194/tc-17-5477-2023.

EUMETSAT, 2023: Animations from Europe's first lightning imager. https://www.eumetsat.int/features/animations-europes-first-lightning-imager.

—, 2024: MTG lightning imager data available. https://user.eumetsat.int/news-events/news/mtg-lightning-imager-level-2-data-available.

Feng, L., P. I. Palmer, S. Zhu, R. J. Parker, and Y. Liu, 2022: Tropical methane emissions explain large fraction of recent changes in global atmospheric methane growth rate. *Nat. Commun.*, **13**, 1378, https://doi.org/10.1038/s41467-022-28989-z.

Feng, Y., and Coauthors, 2022: Decadal lake volume changes (2003–2020) and driving forces at a global scale. *Remote Sens.*, **14**, 1032, https://doi.org/10.3390/rs14041032.

Feron, S., and Coauthors, 2024: South America is becoming warmer, drier, and more flammable. *Commun. Earth Environ.*, **5**, 501, https://doi.org/10.1038/s43247-024-01654-7.

Finlay, C. C., C. Kloss, N. Olsen, M. D. Hammer, L. Toffner-Clausen, A. Grayver, and A. Kuvshinov, 2020: The CHAOS-7 geomagnetic field model and observed changes in the South Atlantic anomaly. *Earth Planets Space*, **72**, 156, https://doi.org/10.1186/s40623-020-01252-9.

Fischer, E. V., and Coauthors, 2014: Atmospheric peroxyacetyl nitrate (PAN): A global budget and source attribution. *Atmos. Chem. Phys.*, **14**, 2679–2698, https://doi.org/10.5194/acp-14-2679-2014.

Fisher, J. B., and Coauthors, 2020: ECOSTRESS: NASA's next generation mission to measure evapotranspiration from the international space station. *Water Resour. Res.*, **56**, e2019WR026058, https://doi.org/10.1029/2019WR026058.

Fleming, E. L., P. A. Newman, Q. Liang, and L. D. Oman, 2024: Stratospheric temperature and ozone impacts of the huna Tonga-hunga Ha'apai water vapor injection. *J. Geophys. Res. Atmos.*, **129**, e2023JD039298, https://doi.org/10.1029/2023JD039298.

Fogt, R. L., and G. J. Marshall, 2020: The Southern annular mode: Variability, trends, and climate impacts across the Southern Hemisphere. *Wiley Interdiscip. Rev.: Climate Change*, **11**, e652, https://doi.org/10.1002/wcc.652.
Folland, C. K., J. Knight, H. W. Linderholm, D. Fereday, S. Ineson, and J. W. Hurrell, 2009: The summer North Atlantic oscillation: Past, present, and future. *J. Climate*, **22**, 1082–1103, https://doi.org/10.1175/2008JCLI2459.1.

Forster, P., and Coauthors, 2021: The Earth's energy budget, climate feedbacks, and climate sensitivity. *Climate Change 2021: The Physical Science Basis*, V. Masson-Delmotte et al., Eds., Cambridge University Press, 923–1054, https://doi.org/10.1017/9781009157896.009.

Forster, P. M., and Coauthors, 2023: Indicators of global climate change 2022: Annual update of large-scale indicators of the state of the climate system and human influence. *Earth Syst. Sci. Data*, **15**, 2295–2327, https://doi.org/10.5194/essd-15-2295-2023.

—, and Coauthors, 2024: Indicators of global climate change 2023: Annual update of key indicators of the state of the climate system and human influence. *Earth Syst. Sci. Data*, **16**, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024.

Foster, M. J., C. Phillips, A. K. Heidinger, E. E. Borbas, Y. Li, W. P. Menzel, A. Walther, and E. Weisz, 2023: PATMOS-x version 6.0: 40 years of merged AVHRR and HIRS global cloud data. *J. Climate*, **36**, 1143–1160, https://doi.org/10.1175/JCLI-D-22-0147.1.

Free, M., D. J. Seidel, J. K. Angel, J. Lanzante, I. Durre, and T. C. Peterson, 2005: Radiosonde atmospheric temperature products for assessing climate (RATPAC): A new data set of large-area anomaly time series. *J. Geophys. Res.*, **110**, D22101, https://doi.org/10.1029/2005JD006169.

Friedlingstein, P., and Coauthors, 2025: Global Carbon Budget 2024. *Earth Syst. Sci. Data*, **17**, 965–1039, https://doi.org/10.5194/essd-17-965-2025.

Frost, G. V., and Coauthors, 2024: NOAA Arctic Report Card 2024: Tundra greenness. NOAA Tech. Rep. OAR ARC 24-09, 9 pp., https://doi.org/10.25923/5t2g-fm41.

Füllekrug, M., E. Williams, and C. Price, 2002: Intense oceanic lightning. *Ann. Geophys.*, **20**, 133–137, https://doi.org/10.5194/angeo-20-133-2002.

—, —, —, S. Goodman, R. Holzworth, K. Virts, and D. Buechler, 2022: Sidebar 2.1: Lightning [in "State of the Climate in 2021"). *Bull. Amer. Meteor. Soc.*, **103** (8), S79, https://doi.org/10.1175/2022BAMSStateoftheClimate.1.

Gao, R. S., and Coauthors, 2016: A light-weight, high-sensitivity particle spectrometer for PM₂.₅ aerosol measurements. *Aerosol Sci. Technol.*, **50**, 88–99, https://doi.org/10.1080/02786826.2015.1131809.

Garfinkel, C. I., and Coauthors, 2021: Influence of the El Niño–Southern Oscillation on entry stratospheric water vapor in coupled chemistry-ocean CCMI and CMIP6 models. *Atmos. Chem. Phys.*, **21**, 3725–3740, https://doi.org/10.5194/acp-21-3725-2021.

Garreaud, R., and Coauthors, 2025: Hyperdroughts in central Chile: Drivers, impacts and projections. *EGUsphere*, https://doi.org/10.5194/egusphere-2025-517.

Garreaud, R. D., J. P. Boisier, R. Rondanelli, A. Montecinos, H. H. Sepúlveda, and D. Veloso-Aguila, 2020: The central Chile mega drought (2010–2018): A climate dynamics perspective. *Int. J. Climatol.*, **40**, 421–439, https://doi.org/10.1002/joc.6219.

Gaudel, A., and Coauthors, 2018: Tropospheric ozone assessment report: Present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation. *Elementa*, **6**, 39, https://doi.org/10.1525/elementa.291.

GCOS, 2022: The 2022 Global Climate Observing System (GCOS) implementation plan. WMO GCOS-244, 98 pp., https://gcos.wmo.int/en/publications/gcos-implementation-plan2022.

Gelaro, R., and Coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). *J. Climate*, **30**, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1.

Getirana, A., S. Kumar, M. Girotto, and M. Rodell, 2017: Rivers and floodplains as key components of global terrestrial water storage variability. *Geophys. Res. Lett.*, **44**, 10359–10310, https://doi.org/10.1002/2017GL074684.

Gettelman, A., and Coauthors, 2024: Has reducing ship emissions brought forward global warming? *Geophys. Res. Lett.*, **51**, e2024GL109077, https://doi.org/10.1029/2024GL109077.

Ghent, D. J., J. S. Anand, K. Veal, and J. J. Remedios, 2024: The operational and climate land surface temperature products from the sea and land surface temperature radiometers on Sentinel-3A and 3B. *Remote Sens.*, **16**, 3403, https://doi.org/10.3390/rs16183403.

Giglio, L., W. Schroeder, and C. O. Justice, 2016: The Collection 6 MODIS active fire detection algorithm and fire products. *Remote Sens. Environ.*, **178**, 31–41, https://doi.org/10.1016/j.rse.2016.02.054.

GISTEMP Team, 2025: GISS Surface Temperature Analysis (GIS-TEMP), version 4. NASA Goddard Institute for Space Studies, accessed 20 January 2025, https://data.giss.nasa.gov/gistemp/.

Gobron, N., B. Belward, P. Pinty, and W. Knorr, 2010: Monitoring biosphere vegetation 1998–2009. *Geophys. Res. Lett.*, **37**, L15402, https://doi.org/10.1029/2010GL043870.

—, and Coauthors, 2022: Evaluation of Sentinel-3A and Sentinel-3B ocean land colour instrument green instantaneous fraction of absorbed photosynthetically active radiation. *Remote Sens. Environ.*, **270**, 112850, https://doi.org/10.1016/j.rse.2021.112850.

Goessling, H. F., T. Rackow, and T. Jung, 2025: Recent global temperature surge intensified by record-low planetary albedo. *Science*, **387**, 68–73, https://doi.org/10.1126/science.adq7280.

Good, E. J., 2016: An in situ–based analysis of the relationship between land surface "skin" and screen-level air temperatures. *J. Geophys. Res. Atmos.*, **121**, 8801–8819, https://doi.org/10.1002/2016JD025318.

—, D. J. Ghent, C. E. Bulgin, and J. J. Remedios, 2017: A spatio-temporal analysis of the relationship between near-surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series. *J. Geophys. Res. Atmos.*, **122**, 9185–9210, https://doi.org/10.1002/2017JD026880.

—, F. M. Aldred, D. J. Ghent, K. L. Veal, and C. Jimenez, 2022: An analysis of the stability and trends in the LSTCCI land surface temperature datasets over Europe. *Earth Space Sci.*, **9**, e2022EA002317, https://doi.org/10.1029/2022EA002317.

Granier, C., and Coauthors, 2011: Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period. *Climatic Change*, **109**, 163–190, https://doi.org/10.1007/s10584-011-0154-1.

Gruber, A., W. A. Dorigo, W. Crow, and W. Wagner, 2017: Triple Collocation-Based merging of satellite soil moisture retrievals. *IEEE Trans. Geosci. Remote Sens.*, **55**, 6780–6792, https://doi.org/10.1109/TGRS.2017.2734070.

—, T. Scanlon, R. van der Schalie, W. Wagner, and W. Dorigo, 2019: Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology. *Earth Syst. Sci. Data*, **11**, 717–739, https://doi.org/10.5194/essd-11-717-2019.
Gulev, S. K., and Coauthors, 2021: Changing state of the climate system. Climate Change 2021: The Physical Science Basis, V. Masson-Delmotte et al., Eds., Cambridge University Press, 287–422, https://doi.org/10.1017/9781009157896.004.

Gyawali, M. S., and Coauthors, 2023: Tracking NO₂ pollution changes over Texas: Synthesis of in situ and satellite observations. *J. Geophys. Res. Atmos.*, **128**, e2022JD037473, https://doi.org/10.1029/2022JD037473.

Haimberger, L., C. Tavolato, and S. Sperka, 2012: Homogenization of the global radiosonde temperature dataset through combined comparison with reanalysis background series and neighboring stations. *J. Climate*, **25**, 8108–8131, https://doi.org/10.1175/JCLI-D-11-00668.1.

Han, Y., Q. Lin, S. Huang, C. Du, J. Shen, and K. Zhang, 2024: Human impacts dominate global loss of lake ecosystem resilience. *Geophys. Res. Lett.*, **51**, e2024GL109298, https://doi.org/10.1029/2024GL109298.

Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. *Rev. Geophys.*, **48**, RG4004, https://doi.org/10.1029/2010RG000345.

Harris, I., T. J. Osborn, P. D. Jones, and D. H. Lister, 2020: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. *Sci. Data*, **7**, 109, https://doi.org/10.1038/s41597-020-0453-3.

Hassan, T., R. Gulzar, M. Hamid, R. Ahmad, S. A. Waza, and A. A. Khuroo, 2024: Plant phenology shifts under climate warming: A systematic review of recent scientific literature. *Environ. Monit. Assess.*, **196**, 36, https://doi.org/10.1007/s10661-023-12190-w

Hattersley, R., and Coauthors, 2023: SciTools/iris: v3.7.0 (v3.7.0). Zenodo, https://doi.org/10.5281/zenodo.8305232.

He, J., and Coauthors, 2024: COVID-19 perturbation on US air quality and human health impact assessment. *PNAS Nexus*, **3**, pgad483, https://doi.org/10.1093/pnasexus/pgad483.

Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. *Quart. J. Roy. Meteor. Soc.*, **146**, 1999–2049, https://doi.org/10.1002/qj.3803.

Hirschi, M., and Coauthors, 2024: Soil moisture [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), Si-S484, https://doi.org/10.1175/2024BAMSStateoftheClimate.1.

Ho, S.-P., and Coauthors, 2020: The COSMIC/FORMOSAT-3 radio occultation mission after 12 years: Accomplishments, remaining challenges, and potential impacts of COSMIC-2. *Bul. Amer. Meteor. Sci.*, **101**, E1107–E1136, https://doi.org/10.1175/BAMS-D-18-0290.1.

Hodnebrog, Ø., and Coauthors, 2020a: Updated global warming potentials and radiative efficiencies of halocarbons and other weak atmospheric absorbers. *Rev. Geophys.*, **58**, e2019RG000691, https://doi.org/10.1029/2019RG000691.

—, and Coauthors, 2020b: The effect of rapid adjustments to halocarbons and N₂O on radiative forcing. *npj Climate Atmos. Sci.*, **3**, 43, https://doi.org/10.1038/s41612-020-00150-x.

Hoell, A., C. Funk, J. Zinke, and L. Harrison, 2017: Modulation of the southern Africa precipitation response to the El Niño Southern Oscillation by the subtropical Indian Ocean dipole. *Climate Dyn.*, **48**, 2529–2540, https://doi.org/10.1007/s00382-016-3220-6.

Hofmann, D. J., J. H. Butler, E. J. Dlugokencky, J. W. Elkins, K. Masarie, S. A. Montzka, and P. Tans, 2006: The role of carbon dioxide in climate forcing from 1979 to 2004: Introduction of the annual greenhouse gas index. *Tellus*, **58B**, 614–619, https://doi.org/10.1111/j.1600-0889.2006.00201.x.

Holzworth, R. H., M. P. McCarthy, J. B. Brundell, A. R. Jacobson, and C. J. Rodger, 2019: Global distribution of superbolts. *J. Geo-phys. Res. Atmos.*, **124**, 9996–10005, https://doi.org/10.1029/2019JD030975.

—, J. B. Brundell, M. P. McCarthy, A. R. Jacobson, C. J. Rodger, and T. S. Anderson, 2021: Lightning in the Arctic. *Geophys. Res. Lett.*, **48**, e2020GL091366, https://doi.org/10.1029/2020GL091366.

Hou, J., A. I. J. M. Van Dijk, L. J. Renzullo, and P. R. Larraondo, 2024: GloLakes: Water storage dynamics for 27 000 Lakes globally from 1984 to present derived from satellite altimetry and optical imaging. *Earth Syst. Sci. Data*, **16**, 201–218, https://doi.org/10.5194/essd-16-201-2024.

Hrbáček, F., and Coauthors, 2023: Active layer and permafrost thermal regimes in the ice-free areas of Antarctica. *Earth-Sci. Rev.*, **242**, 104458, https://doi.org/10.1016/j.earsci-rev.2023.104458.

Hu, G., and Coauthors, 2024: Spatiotemporal characteristics and variability in the thermal state of permafrost on the Qinghai–Tibet Plateau. *Permafrost Periglacial Processes*, **35**, 143–156, https://doi.org/10.1002/ppp.2219.

Hu, Y., and Coauthors, 2025: Rock glacier velocity: An essential climate variable quantity for permafrost. *Rev. Geophys.*, **63**, e2024RG000847, https://doi.org/10.1029/2024RG000847.

Huang, X., K. Yang, S. Kondragunta, Z. Wei, L. Valin, J. Szykman, and M. Goldberg, 2022: NO₂ retrievals from NOAA-20 OMPS: Algorithm, evaluation, and observations of drastic changes during COVID-19. *Atmos. Environ.*, **290**, 119367, https://doi.org/10.1016/j.atmosenv.2022.119367.

Huffman, G. J., R. Adler, A. Behrangi, D. Bolvin, E. Nelkin, G. Gu, and M. Ehsani, 2023: The new version 3.2 Global Precipitation Climatology Project (GPCP) Monthly and Daily precipitation products. *J. Climate*, **36**, 7635–7655, https://doi.org/10.1175/JCLI-D-23-0123.1.

Hunter, J. D., 2007: Matplotlib: A 2D graphics environment. *Comput. Sci. Eng.*, **9**, 90–95, https://doi.org/10.1109/MCSE.2007.55.

Hurst, D. F., S. J. Oltmans, H. Vömel, K. H. Rosenlof, S. M. Davis, E. A. Ray, E. G. Hall, and A. F. Jordan, 2011: Stratospheric water vapor trends over boulder, Colorado: Analysis of the 30 year boulder record. *J. Geophys. Res.*, **116**, D02306, https://doi.org/10.1029/2010JD015065.

Ingram, W., 2010: A very simple model for the water vapour feedback on climate change. *Quart. J. Roy. Meteor. Soc.*, **136**, 30–40, https://doi.org/10.1002/qj.546.

Inness, A., and Coauthors, 2019: The CAMS reanalysis of atmospheric composition. *Atmos. Chem. Phys.*, **19**, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019.

IPCC, 2013: *Climate Change 2013: The Physical Science Basis*. T. F. Stocker et al., Eds., Cambridge University Press, 1535 pp.

—, 2021: Annex VII: Glossary. *Climate Change 2021: The Physical Science Basis*, V. Masson-Delmotte et al., Eds., Cambridge University Press, 2215–2256, https://doi.org/10.1017/9781009157896.022.

Isaksen, K., and Coauthors, 2022: Exceptional warming over the Barents area. *Sci. Rep.*, **12**, 9371, https://doi.org/10.1038/s41598-022-13568-5.

Jacobs, N. A., 2021: Open innovation and the case for community model development. *Bull. Amer. Meteor. Soc.*, **102**, E2002–E2011, https://doi.org/10.1175/BAMS-D-21-0030.1.
Jasinski, M. F., and Coauthors, 2023: ATLAS/ICESat-2 L3A along track inland surface water data, version 6. NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 31 December 2024, https://doi.org/10.5067/ATLAS/ ATL13.006.

John, V. O., L. Shi, E.-S. Chung, R. P. Allan, S. A. Buehler, and B. J. Soden, 2024: Upper tropospheric humidity [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S59–60, https://doi.org/10.1175/2024BAMSStateoftheClimate.1.

Joint Research Centre–European Commission, 2025: Global flood awareness system. Accessed 12 February 2025, https://glob- al-flood.emergency.copernicus.eu/.

Juncosa Calahorrano, J. F., V. H. Payne, S. Kulawik, B. Ford, F. Flocke, T. Campos, and E. V. Fischer, 2021: Evolution of acyl peroxyni- trates (PANs) in wildfire smoke plumes detected by the cross- track infrared sounder (CrIS) over the western U.S. during sum- mer 2018. *Geophys. Res. Lett.*, **48**, e2021GL093405, https:// doi.org/10.1029/2021GL093405.

Junod, R. A., and J. R. Christy, 2020: A new compilation of globally gridded night-time marine air temperatures: The UAHNMATv1 dataset. *Int. J. Climatol.*, **40**, 2609–2623, https:// doi.org/10.1002/joc.6354.

Kääb, A., and J. Röste, 2024: Rock glaciers across the United States predominantly accelerate coincident with rise in air tempera- tures. *Nat. Commun.*, **15**, 7581, https://doi.org/10.1038/ s41467-024-52093-z.

—, T. Strozzi, T. Bolch, R. Caduff, H. Trefall, M. Stoffel, and A. Kokarev, 2021: Inventory and changes of rock glacier creep speeds in Ile Alatau and Kungöy Ala-Too, Northern Tien Shan, since the 1950s. *Cryosphere*, **15**, 927–949, https://doi. org/10.5194/tc-15-927-2021.

Kainz, M. J., R., Ptacnik, S., Rasconi, and H. H. Hager, 2017: Irregu- lar changes in lake surface water temperature and ice cover in subalpine Lake Lunz, Austria. *Inland Waters*, **7**, 27–33, https:// doi.org/10.1080/20442041.2017.1294332.

Kaiser, J. W., and Coauthors, 2012: Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. *Biogeosciences*, **9**, 527–554, https://doi.org/10.5194/bg-9-527-2012.

Kamal, M., T. I. Sanjid, A. Rony, and F. Piya, 2024: The August 2024 Bangladesh flood: Examining meteorological trig- gers, forecasting limitations, and Cross-Border water dy- namics. ESS Open Archive, https://doi.org/10.22541/es- soar.173257596.62658483/v1.

Kaplan, A., 2011: Patterns and indices of climate variability [in "State of the Climate in 2010"). *Bull. Amer. Meteor. Soc.*, **92** (6), S20–S25, https://doi.org/10.1175/1520-0477-92.6.S1.

Keller, C. A., and Coauthors, 2021: Description of the NASA GEOS composition forecast modeling system GEOS-CF v1.0. *J. Adv. Model. Earth Syst.*, **13**, e2020MS002413, https://doi. org/10.1029/2020MS002413.

Kellerer-Pirklbauer, A., and Coauthors, 2024: Acceleration and interannual variability of creep rates in Mountain permafrost landforms (rock glacier velocities) in the European Alps over the time period 1995–2022 based on geodetic surveys: Clini- matic factors versus site-specific effects. *Environ. Res. Lett.*, **19**, 034022, https://doi.org/10.1088/1748-9326/ad25a4.

Kennedy, J. J., N. A. Rayner, C. P. Atkinson, and R. E. Killick, 2019: An ensemble data set of sea surface temperature change from 1850: The Met Office Hadley Centre HadSST.4.0.0.0 data set. *J. Geophys. Res. Atmos.*, **124**, 7719–7763, https://doi. org/10.1029/2018JD029867.

Khykin, S., and Coauthors, 2020: The 2019/20 Australian wild- fires generated a persistent smoke-charged vortex rising up to 35 km altitude. *Commun. Earth Environ.*, **1**, 22, https://doi. org/10.1038/s43247-020-00022-5.

—, and Coauthors, 2022: Global perturbation of stratospher- ic water and aerosol burden by Hunga eruption. *Commun. Earth Environ.*, **3**, 316, https://doi.org/10.1038/s43247-022- 00652-x.

—, and Coauthors, 2024: Stratospheric aerosols [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S96–S98, https://doi.org/10.1175/BAMS-D-24-0116.1.

Khykin, S. M., S. Godin-Beekmann, A. Hauchecorne, J. Pelon, F. Ravetta, and P. Keckhut, 2018: Stratospheric smoke with unprecedentedly high backscatter observed by lidars above Southern France. *Geophys. Res. Lett.*, **45**, 1639–1646, https:// doi.org/10.1002/2017GL076763.

Kleipool, Q., and Coauthors, 2022: Ozone Monitoring Instrument (OMI) collection 4: Establishing a 17-year-long series of de- trended level-1b data. *Atmos. Meas. Tech.*, **15**, 3527–3553, https://doi.org/10.5194/amt-15-3527-2022.

Kobayashi, S., and Coauthors, 2015: The JRA-55 reanalysis: Gener- al specifications and basic characteristics. *J. Meteor. Soc. Japan*, **93**, 5–48, https://doi.org/10.2151/jmsj.2015-001.

Kodera, K., H. Mukougawa, P. Maury, M. Ueda, and C. Claud, 2016: Absorbing and reflecting sudden stratospheric warming events and their relationship with tropospheric circulation. *J. Geophys. Res. Atmos.*, **121**, 80–94, https://doi.org/10.1002/ 2015JD023359.

Konopka, P., M. Tao, F. Ploeger, D. F. Hurst, M. L. Santee, J. S. Wright, and M. Riese, 2022: Stratospheric moistening after 2000. *Geophys. Res. Lett.*, **49**, e2021GL097609, https://doi. org/10.1029/2021GL097609.

Kosaka, Y., and Coauthors, 2024: The JRA-3Q reanalysis. *J. Meteor. Soc. Japan*, **102**, 49–109, https://doi.org/10.2151/jmsj.2024- 004.

Kothari, R., 2024: Cleanup begins in Valencia after Spain’s worst floods in history claim over 200 lives. The Watchers, https:// watchers.news/2024/11/01/valencia-cleanup-spain-worst -flood-record-october-2024/.

Kovilakam, M., L. W. Thomason, N. Ernest, L. Rieger, A. Bouras- sa, and L. Millán, 2020: The global space-based stratospheric aerosol climatology (version 2.0): 1979–2018. *Earth Syst. Sci. Data*, **12**, 2607–2634, https://doi.org/10.5194/essd-12-2607- 2020.

Kraemer, B. M., A. Seimon, R. Adrian, and P. B. McIntyre, 2020: Worldwide lake level trends and responses to background cli- mate variation. *Hydrol. Earth Syst. Sci.*, **24**, 2593–2608, https:// doi.org/10.5194/hess-24-2593-2020.

Kratz, D. P., P. W. Stackhouse Jr., S. K. Gupta, A. C. Wilber, P. Sawaengphokhai, and G. R. McGarragh, 2014: The fast longwave and shortwave flux (FLASHFlux) data product: Single-Scanner footprint fluxes. *J. Appl. Meteor. Climatol.*, **53**, 1059–1079, https://doi.org/10.1175/JAMC-D-13-061.1.

Kremser, S., and Coauthors, 2016: Stratospheric aerosol- observations, processes, and impact on climate. *Rev. Geophys.*, **54**, 278–335, https://doi.org/10.1002/2015RG000511.

Lagouarde, J.-P., and Coauthors, 2018: The Indian-French TRISHNA Mission: Earth Observation in the thermal infrared with high spatio-temporal resolution. *IGARSS 2018 – 2018 IEEE Int. Geo- science and Remote Sensing Symp.*, Valencia, Spain, Institute of Electrical and Electronics Engineers, 4078–4081, https://doi. org/10.1109/IGARSS.2018.8518720.
Lambiel, C., and R. Delaloye, 2004: Contribution of real-time kinematic GPS in the study of creeping Mountain permafrost: Examples from the western Swiss Alps. *Permafrost Periglacial Processes*, **15**, 229–241, https://doi.org/10.1002/ppp.496.

Lan, X., and Coauthors, 2019: Long-term measurements show little evidence for large increases in total U.S. methane emissions over the past decade. *Geophys. Res. Lett.*, **46**, 4991–4999, https://doi.org/10.1029/2018GL081731.

—, and Coauthors, 2021: Improved constraints on global methane emissions and sinks using $\delta^{13}$C-$CH_4$. *Global Biogeochem. Cycles*, **35**, e2021GB007000, https://doi.org/10.1029/2021GB007000.

—, P. Tans, and K. W. Thoning, 2025a: Trends in globally-averaged $CO_2$ determined from NOAA global monitoring laboratory measurements, version 2025-05. Accessed 1 June 2025, https://doi.org/10.15138/9N0H-ZH07.

—, K. W. Thoning, and E. J. Dlugokencky, 2025b: Trends in globally-averaged $CH_4$, $N_2O$, and $SF_6$ determined from NOAA global monitoring laboratory measurements, version 2025-05. Accessed 1 June 2025, https://doi.org/10.15138/P8XG-AA10.

Landerer, F. W., and Coauthors, 2020: Extending the global mass change data record: GRACE follow-on instrument and science data performance. *Geophys. Res. Lett.*, **47**, e2020GL088306, https://doi.org/10.1029/2020GL088306.

Lavers, D. A., A. Simmons, F. Vamborg, and M. J. Rodwell, 2022: An evaluation of ERA5 precipitation for climate monitoring. *Quart. J. Roy. Meteor. Soc.*, **148**, 3152–3165, https://doi.org/10.1002/qj.4351.

Lee, H.-T., and NOAA CDR Program, 2018: NOAA Climate Data Record (CDR) of Monthly Outgoing Longwave Radiation (OLR), version 2.7. NOAA National Centers for Environmental Information, accessed 5 January 2023, https://doi.org/10.7289/V5W37TKD.

Lee, S. H., A. H. Butler, and G. L. Manney, 2025: Two major sudden stratospheric warmings during winter 2023/2024. *Weather*, **80**, 45–53, https://doi.org/10.1002/wea.7656.

Lehner, B., and Coauthors, 2011: High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management. *Front. Ecol. Environ.*, **9**, 494–502, https://doi.org/10.1890/100125.

Lenssen, N. J. L., G. A. Schmidt, J. E. Hansen, M. J. Menne, A. Persin, R. Ruedy, and D. Zyss, 2019: Improvements in the GISTEMP uncertainty model. *J. Geophys. Res. Atmos.*, **124**, 6307–6326, https://doi.org/10.1029/2018JD029522.

Levene, H., 1960: Robust tests for equality of variances. *Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling*, Ingram Olkin, Harold Hotelling et al., Eds., Stanford University Press, 278–292.

Levy, R. C., S. Mattoo, L. A. Munchak, L. A. Remer, A. M. Sayer, F. Patadia, and N. C. Hsu, 2013: The Collection 6 MODIS aerosol products over land and ocean. *Atmos. Meas. Tech.*, **6**, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013.

L'Heureux, M. L., D. S. Harnos, E. Becker, B. Brettschneider, M. Chen, N. C. Johnson, A. Kumar, and M. K. Tippett, 2024: How well do seasonal climate anomalies match expected El Niño–Southern Oscillation (ENSO) impacts? *Bull. Amer. Meteor. Soc.*, **105**, E1542–E1551, https://doi.org/10.1175/BAMS-D-23-0252.1.

Lin, X., and Coauthors, 2024: Recent methane surges reveal heightened emissions from tropical inundated areas. *Nat. Commun.*, **15**, 10894, https://doi.org/10.1038/s41467-024-55266-y.

Lindaas, J., and Coauthors, 2021: Emissions of reactive nitrogen from western U.S. wildfires during summer 2018. *J. Geophys. Res. Atmos.*, **126**, e2020JD032657, https://doi.org/10.1029/2020JD032657.

Liu, J., and Coauthors, 2017: Contrasting carbon cycle responses of the tropical continents to the 2015–2016 El Niño. *Science*, **358**, eaam5690, https://doi.org/10.1126/science.aam5690.

Liu, R., X. Zhang, W. Wang, Y. Wang, H. Liu, M. Ma, and G. Tang, 2024: Global-scale ERA5 product precipitation and temperature evaluation. *Ecol. Indic.*, **166**, 112481, https://doi.org/10.1016/j.ecolind.2024.112481.

Liu, Y., and Coauthors, 2017: Evaluation of the VIIRS BRDF, albedo and NBAR products suite and an assessment of continuity with the long term MODIS record. *Remote Sens. Environ.*, **201**, 256–274, https://doi.org/10.1016/j.rse.2017.09.020.

—, L. Zhou, Y. Qin, C. Azorin-Molina, C. Shen, R. Xu, and Z. Zeng, 2024: Impacts of anemometer changes, site relocations and processing methods on wind speed trends in China. *Atmos. Meas. Tech.*, **17**, 1123–1131, https://doi.org/10.5194/amt-17-1123-2024.

Loeb, N. G., B. A. Wielicki, D. R. Doelling, G. L. Smith, D. F. Keyes, S. Kato, N. Manalo-Smith, and T. Wong, 2009: Toward optimal closure of the earth's top-of-atmosphere radiation budget. *J. Climate*, **22**, 748–766, https://doi.org/10.1175/2008JCLI2637.1.

—, S. Kato, W. Su, T. Wong, F. G. Rose, D. R. Doelling, J. R. Norris, and X. Huang, 2012: Advances in understanding top-of-atmosphere radiation variability from satellite observations. *Surv. Geophys.*, **33**, 359–385, https://doi.org/10.1007/s10712-012-9175-1.

—, and Coauthors, 2018: Clouds and the Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) Top-of-Atmosphere (TOA) edition-4.0 data product. *J. Climate*, **31**, 895–918, https://doi.org/10.1175/JCLI-D-17-0208.1.

—, G. C. Johnson, T. J. Thorsen, J. M. Lyman, F. G. Rose, and S. Kato, 2021: Satellite and ocean data reveal marked increase in earth's heating rate. *Geophys. Res. Lett.*, **48**, e2021GL093047, https://doi.org/10.1029/2021GL093047.

—, and Coauthors, 2022: Evaluating twenty-year trends in earth's energy flows from observations and reanalyses. *J. Geophys. Res. Atmos.*, **127**, e2022JD036686, https://doi.org/10.1029/2022JD036686.

MacCallum, S. N., and C. J. Merchant, 2012: Surface water temperature observations of large Lakes by optimal estimation. *Can. J. Remote Sens.*, **38**, 25–45, https://doi.org/10.5589/m12-010.

Maciel, D. A., Lousada, F. Fassoni-Andrade, A. Pacheco Quevedo, R. Barbosa, C. C. F. Paule-Bonnet, and E. M. L. D. M. Novo, 2024: Sentinel-1 data reveals unprecedented reduction of open water extent due to 2023-2024 drought in the central Amazon basin. *Environ. Res. Lett.*, **19**, 124034, https://doi.org/10.1088/1748-9326/ad8a71.

Magnin, F., L. Ravanel, X. Bodin, P. Deline, E. Malet, J. Krysicki, and P. Schoeneich, 2023: Main results of permafrost monitoring in the French Alps through the PermaFrance network over the period 2010–2022. *Permafrost Periglacial Processes*, **35**, 3–23, https://doi.org/10.1002/ppp.2209.

Magnusson, L., and Coauthors, 2025: Severe rain in central Europe from Storm Boris. ECMWF, accessed 29 May 2025, https://www.ecmwf.int/en/newsletter/182/news/severe-rain-central-europe-storm-boris.

Marengo, J., and Coauthors, 2024: The drought of Amazonia in 2023-2024. *Amer. J. Climate Change*, **13**, 567–597, https://doi.org/10.4236/ajcc.2024.133026.
Martens, B., and Coauthors, 2017: GLEAM v3: Satellite-based land evaporation and root-zone soil moisture. *Geosci. Model Dev.*, **10**, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017.

—, W. Waegeman, W. A. Dorigo, N. E. C. Verhoest, and D. G. Miralles, 2018: Terrestrial evaporation response to modes of climate variability. *npj Climate Atmos. Sci.*, **1**, 43, https://doi.org/10.1038/s41612-018-0053-5.

Matthews, T., M. Byrne, R. Horton, C. Murphy, R. Pielke Sr., C. Raymond, P. Thorne, and R. L. Wilby, 2022: Latent heat must be visible in climate communications. *Wiley Interdiscip. Rev.: Climate Change*, **13**, e779, https://doi.org/10.1002/wcc.779.

—, —, P. C. Stoy, and K. M. Willett, 2024: Sidebar 2.2: Near-surface equivalent temperature as a key climate change metric [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S30–S32, https://doi.org/10.1175/2024BAMS-StateoftheClimate.1.

—, C. Raymond, J. Foster, J. W. Baldwin, C. Ivanovich, Q. Kong, P. Kinney, and R. M. Horton, 2025: Mortality impacts of the most extreme heat events. *Nat. Rev. Earth Environ.*, **6**, 193–210, https://doi.org/10.1038/s43017-024-00635-w.

Mayer, M., L. Haimberger, C. T. Sabeerali, V. Schenzinger, D. E. Surendran, and O. P. Sreejith, 2023: Upper air winds [in "State of the Climate in 2022"). *Bull. Amer. Meteor. Soc.*, **104** (9), S74–S76, https://doi.org/10.1175/BAMS-D-23-0090.1.

McCabe, M. F., B. Aragon, R. Houborg, and J. Mascaro, 2017: CubeSats in hydrology: Ultrahigh-resolution insights into vegetation dynamics and terrestrial evaporation. *Water Resour. Res.*, **53**, 10017–10024, https://doi.org/10.1002/2017WR022240.

McCarthy, M. P., and R. Toumi, 2004: Observed interannual variability of tropical troposphere relative humidity. *J. Climate*, **17**, 3181–3191, https://doi.org/10.1175/1520-0442(2004)017<3181:OIVOTT>2.0.CO;2.

McVicar, T. R., and Coauthors, 2012: Global review and synthesis of trends in observed terrestrial near-surface wind speeds: Implications for evaporation. *J. Hydrol.*, 416–417, 182–205, https://doi.org/10.1016/j.jhydrol.2011.10.024.

Mears, C. A., and F. J. Wentz, 2016: Sensitivity of satellite-derived tropospheric temperature trends to the diurnal cycle adjustment. *J. Climate*, **29**, 3629–3646, https://doi.org/10.1175/JCLI-D-15-0744.1.

—, A. D. Smith, K. L. Ricciardulli, J. Wang, H. Huelsing, and F. J. Wentz, 2018: Construction and uncertainty estimation of a satellite-derived total precipitable water data record over the world's oceans. *Earth Space Sci.*, **5**, 197–210, https://doi.org/10.1002/2018EA000363.

—, J. P. Nicolas, O. Bock, S. P. Ho, and X. Zhou, 2023: Total column water vapor [in "State of the Climate in 2022"). *Bull. Amer. Meteor. Soc.*, **104** (9), S54–S55, https://doi.org/10.1175/BAMS-D-23-0090.1.

Meesters, A. G., R. A. M. De Jeu, and M. Owe, 2005: Analytical derivation of the vegetation optical depth from the microwave polarization difference index. *IEEE Geosci. Remote Sens. Lett.*, **2**, 121–123, https://doi.org/10.1109/LGRS.2005.843983.

Menne, M. J., and Coauthors, 2025: The Global Historical Climatology Network hourly (GHCNh) dataset. *29th Conf. of Applied Climatology*, New Orleans, LA, Amer. Meteor. Soc., 9.1, https://ams.confex.com/ams/105ANNUAL/meetingapp.cgi/Paper/446924.

Menzel, A., Y. Yuan, M. Matiu, T. Sparks, H. Scheifinger, R. Gehrig, and N. Estrella, 2020: Climate change fingerprints in recent European plant phenology. *Global Change Biol.*, **26**, 2599–2612, https://doi.org/10.1111/gcb.15000.

Messager, M. L., B. Lehner, G. Grill, I. Nedeva, and O. Schmitt, 2016: Estimating the volume and age of water stored in global Lakes using a geo-statistical approach. *Nat. Commun.*, **7**, 13603, https://doi.org/10.1038/ncomms13603.

MeteoSwiss, 2025: Klimabulletin Jahr 2024. 14 pp.

Michel, S. E., and Coauthors, 2024: Rapid shift in methane carbon isotopes suggests microbial emissions drove record high atmospheric methane growth in 2020-2022. *Proc. Natl. Acad. Sci. USA*, **121**, e2411212121, https://doi.org/10.1073/pnas.2411212121.

Mildrexler, D. J., M. Zhao, and S. W. Running, 2006: Where are the hottest spots on earth? *Eos, Trans. Amer. Geophys. Union*, **87**, 461–467, https://doi.org/10.1029/2006EO430002.

Millán, L., and Coauthors, 2022: The Hunga Tonga-Hunga Ha'apai hydration of the stratosphere. *Geophys. Res. Lett.*, **49**, e2022GL099381, https://doi.org/10.1029/2022GL099381.

Miller, B. R., and Coauthors, 2010: HFC-23 (CHF₃) emission trend response to HCFC-22 (CHClF₂) production and recent HFC-23 emission abatement measures. *Atmos. Chem. Phys.*, **10**, 7875–7890, https://doi.org/10.5194/acp-10-7875-2010.

Minola, L., H. Reese, H. W. Lai, C. Azorin-Molina, J. A. Guijarro, S. W. Son, and D. Chen, 2022: Wind stilling–reversal across Sweden: The impact of land-use and large-scale atmospheric circulation changes. *Int. J. Climatol.*, **42**, 1049–1071, https://doi.org/10.1002/joc.7289.

Miralles, D. G., and Coauthors, 2014: El Niño–La Niña cycle and recent trends in continental evaporation. *Nat. Climate Change*, **4**, 122–126, https://doi.org/10.1038/nclimate2068.

—, P. Gentine, S. I. Seneviratne, and A. J. Teuling, 2019: Land-atmospheric feedbacks during droughts and heatwaves: State of the science and current challenges. *Ann. N. Y. Acad. Sci.*, **1436**, 19–35, https://doi.org/10.1111/nyas.13912.

—, and Coauthors, 2025: GLEAM4: Global land evaporation and soil moisture dataset at 0.1 resolution from 1980 to near present. *Sci. Data*, **12**, 416, https://doi.org/10.1038/s41597-025-04610-y.

Mitchell, L., E. Brook, J. E. Lee, C. Buizert, and T. Sowers, 2013: Constraints on the late Holocene anthropogenic contribution to the atmospheric methane budget. *Science*, **342**, 964–966, https://doi.org/10.1126/science.1238920.

Moesinger, L., R.-M. Zotta, R. van Der Schalie, T. Scanlon, R. de Jeu, and W. Dorigo, 2022: Monitoring vegetation condition using microwave remote sensing: The standardized vegetation optical depth index (SVODI). *Biogeosciences*, **19**, 5107–5123, https://doi.org/10.5194/bg-19-5107-2022.

Mollaret, C., C. Hilbich, C. Pellet, A. Flores-Orozco, R. Delaloye, and C. Hauck, 2019: Mountain permafrost degradation documented through a network of permanent electrical resistivity tomography sites. *Cryosphere*, **13**, 2557–2578, https://doi.org/10.5194/tc-13-2557-2019.

Monks, P. S., and Coauthors, 2015: Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer. *Atmos. Chem. Phys.*, **15**, 8889–8973, https://doi.org/10.5194/acp-15-8889-2015.

Montreal Protocol, 1989: Montreal Protocol on substances that deplete the ozone layer. Final act 1987. *J. Environ. Law*, **1**, 128–136, https://doi.org/10.1093/jel/1.1.128.

Morán-Tejeda, E., J. L. Ceballos, K. Peña, J. Lorenzo-Lacruz, and J. I. López-Moreno, 2018: Recent evolution and associated hydrological dynamics of a vanishing tropical Andean glacier: Glaciar de Conejeras, Colombia. *Hydrol. Earth Syst. Sci.*, **22**, 5445–5461, https://doi.org/10.5194/hess-22-5445-2018.
Morgenstern, O., and Coauthors, 2025: Radiocarbon monoxide indicates increasing atmospheric oxidizing capacity. *Nat. Commun.*, **16**, 249, https://doi.org/10.1038/s41467-024-55603-1.

Morice, C. P. and Coauthors, 2021: An updated assessment of near-surface temperature change from 1850: The HadCRUT5 data set. *J. Geophys. Res. Atmos.*, **126**, e2019JD032361, https://doi.org/10.1029/2019JD032361.

Mote, P. W., and Coauthors, 1996: An atmospheric tape recorder: The imprint of tropical tropopause temperatures on stratospheric water vapor. *J. Geophys. Res.*, **101**, 3989–4006, https://doi.org/10.1029/95JD03422.

Mühle, J., and Coauthors, 2010: Perfluorocarbons in the global atmosphere: Tetrafluoromethane, hexafluoroethane, and octafluoropropane. *Atmos. Chem. Phys.*, **10**, 5145–5164, https://doi.org/10.5194/acp-10-5145-2010.

Myhre, G., D. Shindell, and J. Pongratz, 2014: Anthropogenic and natural radiative forcing. *Climate Change 2013: The Physical Science Basis*, T. F. Stocker et al., Eds., Cambridge University Press, 659–740.

NASA, 2024: NASA, NOAA: Sun reaches maximum phase in 11-year solar cycle. Accessed 28 February 2025, https://science.nasa.gov/science-research/heliophysics/nasa-noaa-sun-reaches-maximum-phase-in-11-year-solar-cycle/.

NASA Earth Observatory, 2024: Intense, widespread drought grips South America. Accessed 10 February 2025, https://earthobservatory.nasa.gov/images/153447/intense-widespread-drought-grips-south-america.

Newman, P. A., J. S. Daniel, D. W. Waugh, and E. R. Nash, 2007: A new formulation of equivalent effective stratospheric chlorine (EESC). *Atmos. Chem. Phys.*, **7**, 4537–4552, https://doi.org/10.5194/acp-7-4537-2007.

—, L. R. Lait, N. A. Kramarova, L. Coy, S. M. Frith, L. D. Oman, and S. S. Dhomse, 2024: Record high March 2024 Arctic total column ozone. *Geophys. Res. Lett.*, **51**, e2024GL110924, https://doi.org/10.1029/2024GL110924.

Nisbet, E. G., and Coauthors, 2023: Atmospheric methane: Comparison between methane's record in 2006–2022 and during glacial terminations. *Global Biogeochem. Cycles*, **37**, e2023GB007875, https://doi.org/10.1029/2023GB007875.

NOAA, 2024: Great Lakes ice coverage reaches historic low. https://research.noaa.gov/great-lakes-ice-coverage-reaches-historic-low/.

—, 2025: Great Lakes ice cover database 1973-2024. GLERL, accessed 2 June 2025, https://www.glerl.noaa.gov/data/ice/#historical.

NOAA/NCEI, 2024: Monthly Global Climate Report for November 2024. https://www.ncei.noaa.gov/access/monitoring/monthly-report/global/202411#precip.

—, 2025a: Global Climate Report Annual 2024. https://www.ncei.noaa.gov/access/monitoring/monthly-report/global/202413.

—, 2025b: Monthly Drought Report for Annual 2024. Accessed 11 February 2025, https://www.ncei.noaa.gov/access/monitoring/monthly-report/drought/202413.

Noetzli, J., and Coauthors, 2024a: Enhanced warming of European Mountain permafrost in the early 21st century. *Nat. Commun.*, **15**, 10508, https://doi.org/10.1038/s41467-024-54831-9.

Nowak, J. B., and Coauthors, 2012: Ammonia sources in the California South Coast air basin and their impact on ammonium nitrate formation. *Geophys. Res. Lett.*, **39**, L07804, https://doi.org/10.1029/2012GL051197.

O’Gorman, P. A., and C. J. Muller, 2010: How closely do changes in surface and column water vapor follow Clausius–Clapeyron scaling in climate change simulations? *Environ. Res. Lett.*, **5**, 025207, https://doi.org/10.1088/1748-9326/5/2/025207.

Oh, Y., and Coauthors, 2023: CarbonTracker CH$_4$ 2023. Accessed 1 June 2025, https://doi.org/10.25925/40jt-qd67.

Okamoto-Mizuno, K., K. Mizuno, S. Michie, A. Maeda, and S. Iizuka, 1999: Effects of humid heat exposure on human sleep stages and body temperature. *Sleep*, **22**, 767–773, https://doi.org/10.1093/sleep/22.6.767.

O’Kane, T. J., and C. L. Franzke, 2025: Pacific–South American pattern. *Atmospheric Oscillations*, Elsevier, 171–181, https://doi.org/10.1016/B978-0-443-15638-0.00008-3.

O’Keefe, J., and G. VanScoy, 2024: Phenology of woody species at Harvard Forest since 1990. Harvard Forest Data Archive: HF003, accessed 1 February 2025, https://harvardforest1.fas.harvard.edu/exist/apps/datasets/showData.html?id=HF003.

Oman, L. D., A. R. Douglass, J. R. Ziemke, J. M. Rodriguez, D. W. Waugh, and J. E. Nielsen, 2013: The ozone response to ENSO in aura satellite measurements and a chemistry–climate simulation. *J. Geophys. Res. Atmos.*, **118**, 965–976, https://doi.org/10.1029/2012JD018546.

O’Neill, H. B., S. L. Smith, C. R. Burn, C. Duchesne, and Y. Zhang, 2023: Widespread permafrost degradation and thaw subsidence in northwest Canada. *J. Geophys. Res. Earth Surf.*, **128**, e2023JF007262, https://doi.org/10.1029/2023JF007262.

Osborn, T. J., P. D. Jones, D. H. Lister, C. P. Morice, I. R. Simpson, J. P. Winn, E. Hogan, and I. C. Harris, 2021: Land surface air temperature variations across the globe updated to 2019: The CRUTEM5 data set. *J. Geophys. Res. Atmos.*, **126**, e2019JD032352, https://doi.org/10.1029/2019JD032352.

Pakistan Meteorological Department, 2025: State of Pakistan Climate 2024. Climate Data Processing Center, 23 pp., https://cdpc.pmd.gov.pk/Pakistan_Climate_2024.pdf.

Pandey, K., and R. Sengupta, 2024: Climate India 2024: An assessment of extreme weather events. Center for Science and Environment Down to Earth Rep., 60 pp., https://www.cseindia.org/climate-india-2024-an-assessment-of-extreme-weather-events-12460.

Park, T., and Coauthors, 2016: Changes in growing season duration and productivity of Northern vegetation inferred from long-term remote sensing data. *Environ. Res. Lett.*, **11**, 084001, https://doi.org/10.1088/1748-9326/11/8/084001.

—, M. K. Gumma, W. Wang, P. Panjala, S. K. Dubey, and R. R. Nemani, 2023: Greening of human-dominated ecosystems in India. *Commun. Earth Environ.*, **4**, 419, https://doi.org/10.1038/s43247-023-01078-9.

Payne, V. H., and Coauthors, 2022: Satellite measurements of peroxyacetyl nitrate from the Cross-Track infrared sounder: Comparison with ATom aircraft measurements. *Atmos. Meas. Tech.*, **15**, 3497–3511, https://doi.org/10.5194/amt-15-3497-2022.

Pellet, C., and Coauthors, 2024: Rock glacier velocity [in "State of Climate 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), 44–46, https://doi.org/10.1175/BAMS-D-24-0116.1.

Pelto, M., 2024: Alpine Glaciers [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S46–S47, https://doi.org/10.1175/BAMS-D-24-0116.1.

Pelto, M. S., and J. Pelto, 2025: The loss of ice worm glacier, North Cascade range, Washington USA. *Water*, **17**, 432, https://doi.org/10.3390/w17030432.

Peng, S., and Coauthors, 2022: Wetland emission and atmospheric sink changes explain methane growth in 2020. *Nature*, **612**, 477–482, https://doi.org/10.1038/s41586-022-05447-w.
PERMOS, 2024: *Swiss Permafrost Bulletin 2023* (Annual Report of the Swiss Permafrost Monitoring Network PERMOS No. 5). Swiss Permafrost Monitoring Network PERMOS, 25 pp., https://doi.org/10.13093/permos-bull-2024.

—, 2025: *Swiss Permafrost Bulletin 2024* (Annual Report of the Swiss Permafrost Monitoring Network PERMOS No. 6). Swiss Permafrost Monitoring Network PERMOS, https://doi.org/10.13093/permos-bull-2025.

Peterson, D. A., J. Campbell, E. Hyer, M. Fromm, G. Kablick, J. Cossuth, and M. DeLand, 2018: Wildfire-driven thunderstorms cause a volcano-like stratospheric injection of smoke. *npj Climate Atmos. Sci.*, 1, 30, https://doi.org/10.1038/s41612-018-0039-3.

—, and Coauthors, 2021: Australia's Black Summer pyrocumulonimbus super outbreak reveals potential for increasingly extreme stratospheric smoke events. *npj Climate Atmos. Sci.*, 4, 38, https://doi.org/10.1038/s41612-021-00192-9.

Peterson, T. C., K. M. Willett, and P. W. Thorne, 2011: Observed changes in surface atmospheric energy over land. *Geophys. Res. Lett.*, 38, L16707, https://doi.org/10.1029/2011GL048442.

Petropavlovskikh, I., and Coauthors, 2025: Ozone trends in homogenized Umkehr, ozonesonde, and COH overpass records. *Atmos. Chem. Phys.*, 25, 2895-2936, https://doi.org/10.5194/acp-25-2895-2025.

Phillips, C., and M. Foster, 2024: Cloudiness [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, 105 (8), S63-S65, https://doi.org/10.1175/BAMS-D-24-0116.1.

Pi, X., and Coauthors, 2022: Mapping global lake dynamics reveals the emerging roles of small Lakes. *Nat. Commun.*, 13, 5777, https://doi.org/10.1038/s41467-022-33239-3.

Pinto, I., and Coauthors, 2024: Conflict, poverty and water management issues exposing vulnerable communities in Africa to extreme floods that are now common events because of climate change. Imperial College London, 33 pp., https://spiral.imperial.ac.uk/server/api/core/bitstreams/a494f174-d954-4905-9569-0039d372762c/content.

Pinty, B., and Coauthors, 2011: Exploiting the MODIS albedos with the Two-stream Inversion Package (JRC-TIP): 2. Fractions of transmitted and absorbed fluxes in the vegetation and soil layers. *J. Geophys. Res.*, 116, D09106, https://doi.org/10.1029/2010JD015373.

Plummer, D., and Coauthors, 2021: CCMI-2022: A new set of Chemistry-Climate Model Initiative (CCMI) community simulations to update the assessment of models and support upcoming ozone assessment activities. *SPARC Newsletter*, No. 57, 22–30, https://www.sparc-climate.org/wp-content/uploads/sites/5/2021/07/SPARCnewsletter_Jul2021_web.pdf.

Po-Chedley, S., J. R. Christy, L. Haimberger, and C. A. Mears, 2021: Tropospheric temperature [in "State of the Climate in 2020"). *Bull. Amer. Meteor. Soc.*, 102 (8), S34-S37, https://doi.org/10.1175/BAMS-D-21-0098.1.

—, —, —, —, and C.-Z. Zou, 2024: Tropospheric temperature [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, 105 (8), S39-S41, https://doi.org/10.1175/BAMS-D-24-0116.1.

Pogliotti, P., E. Cremonese, and U. Morra di Cella, 2023: Warming permafrost in the western Alps: A further evidence of elevation dependent warming? *J. Alp. Res.*, 111 (2), https://doi.org/10.4000/rga.11784.

Popp, T., and Coauthors, 2016: Development, production and evaluation of aerosol climate data records from European satellite observations (Aerosol_ci). Remote Sens., 8, 421, https://doi.org/10.3390/rs8050421.

Prather, M. J., C. D. Holmes, and J. Hsu, 2012: Reactive greenhouse gas scenarios: Systematic exploration of uncertainties and the role of atmospheric chemistry. *Geophys. Res. Lett.*, 39, L09803, https://doi.org/10.1029/2012GL051440.

Price, C., M. Asfur, and Y. Yair, 2009: Maximum hurricane intensity preceded by increase in lightning frequency. *Nat. Geosci.*, 2, 329-332, https://doi.org/10.1038/ngeo477.

Pucik, T., 2024: Meteorological analysis of extreme flash flood situation in the Valencia region. European Severe Storms Laboratory, accessed 29 May 2025, https://www.essl.org/cms/meteorological-analysis-of-extreme-flash-flood-situation-in-the-valencia-region/.

Raghuraman, S. P., B. Soden, A. Clement, G. Vecchi, S. Menemenlis, and W. Yang, 2024: The 2023 global warming spike was driven by the El Niño-Southern Oscillation. *Atmos. Chem. Phys.*, 24, 11275-11283, https://doi.org/10.5194/acp-24-11275-2024.

Rakov, V. A., and M. A. Uman, 2003: *Lightning Physics and Effects*. Cambridge University Press, 687 pp., https://doi.org/10.1017/CBO9781107340886.

Randel, W. J., and J. B. Cobb, 1994: Coherent variations of monthly mean total ozone and lower stratospheric temperature. *J. Geophys. Res.*, 99, 5433-5447, https://doi.org/10.1029/93JD03454.

—, X. Wang, J. Starr, R. R. Garcia, and D. Kinnison, 2024: Long-term temperature impacts of the Hunga volcanic eruption in the stratosphere and above. *Geophys. Res. Lett.*, 51, e2024GL111500, https://doi.org/10.1029/2024GL111500.

RGIK, 2023a: Guidelines for inventorying rock glaciers: Baseline and practical concepts (version 1.0). IPA Action Group Rock Glacier Inventories and Kinematics, 26 pp., https://doi.org/10.51363/unifr.srr.2023.002.

—, 2023b: Rock glacier velocity as an associated parameter of ECV permafrost: Baseline concepts (version 3.2). IPA Action Group Rock Glacier Inventories and Kinematics, 13 pp.

Ricciardulli, L., and F. J. Wentz, 2015: A scatterometer geophysical model function for climate-quality winds: QuikSCAT Ku-2011. *J. Atmos. Oceanic Technol.*, 32, 1829-1846, https://doi.org/10.1175/JTECH-D-15-0008.1.

—, and A. Manaster, 2021: Intercalibration of ASCAT scatterometer winds from MetOp-A, -B, and -C, for a stable climate data record. *Remote Sens.*, 13, 3678, https://doi.org/10.3390/rs13183678.

Richardson, A. D., 2019: Tracking seasonal rhythms of plants in diverse ecosystems with digital camera imagery. *New Phytol.*, 222, 1742-1750, https://doi.org/10.1111/nph.15591.

—, and J. O'Keeffe, 2009: Phenological differences between understory and overstory. *Phenology of Ecosystem Processes*, A. Noormets, Ed., Springer, 87-117.

Roberts, R., and Coauthors, 2022: Taking the HIGHWAY to save lives on Lake Victoria. *Bull. Amer. Meteor. Soc.*, 103, E485-E510, https://doi.org/10.1175/BAMS-D-20-0290.1.

Rodell, M., and B. Li, 2023: Changing intensity of hydroclimatic extreme events revealed by GRACE and GRACE-FO. Nat. Water, 1, 241-248, https://doi.org/10.1038/s44221-023-00040-5.

—, J. S. Famiglietti, D. N. Wiese, J. T. Reager, H. K. Beaudoing, F. W. Landerer, and M. H. Lo, 2018: Emerging trends in global freshwater availability. *Nature*, 557, 651-659, https://doi.org/10.1038/s41586-018-0123-1.

—, and Coauthors, 2024: An abrupt decline in global terrestrial water storage and its relationship with sea level change. *Surv. Geophys.*, 45, 1875-1902, https://doi.org/10.1007/s10712-024-09860-w.
Rohde, R. A., and Z. Hausfather, 2020: The Berkeley Earth land/ocean temperature record. *Earth Syst. Sci. Data*, **12**, 3469–3479, https://doi.org/10.5194/essd-12-3469-2020.

Rosemartin, A. H., and Coauthors, 2014: Organizing phenological data resources to inform natural resource conservation. *Biol. Conserv.*, **173**, 90–97, https://doi.org/10.1016/j.biocon.2013.07.003.

Rubino, M., and Coauthors, 2019: Revised records of atmospheric trace gases CO₂, CH₄, N₂O, and δ¹³C-CO₂ over the last 2000 years from Law Dome, Antarctica. *Earth Syst. Sci. Data*, **11**, 473–492, https://doi.org/10.5194/essd-11-473-2019.

Rudlosky, S. D., and K. S. Virts, 2021: Dual geostationary lightning mapper observations. *Mon. Wea. Rev.*, **149**, 979–998, https://doi.org/10.1175/MWR-D-20-0242.1.

Rutllant, J. A., and H. Fuenzalida, 1991: Synoptic aspects of the central Chile rainfall variability associated with the Southern Oscillation. *Int. J. Climatol.*, **11**, 63–76, https://doi.org/10.1002/joc.3370110105.

Santee, M. L., and Coauthors, 2023: Strong evidence of heterogeneous processing on stratospheric sulfate aerosol in the extrapolar Southern Hemisphere following the 2022 Hunga Tonga-Hunga Ha'apai eruption. *J. Geophys. Res. Atmos.*, **128**, e2023JD039169, https://doi.org/10.1029/2023JD039169.

Santer, B. D., and Coauthors, 2008: Consistency of modelled and observed temperature trends in the tropical troposphere. *Int. J. Climatol.*, **28**, 1703–1722, https://doi.org/10.1002/joc.1756.

Satheesh, S. K., and Coauthors, 2017: Variability of atmospheric aerosols over India. Observed Climate Variability and Change over the Indian Region, Springer, 221–248, https://doi.org/10.1007/978-981-10-2531-0_13.

Sato, M., J. E. Hansen, M. P. McCormick, and J. B. Pollack, 1993: Stratospheric aerosol optical depths, 1850–1990. *J. Geophys. Res.*, **98**, 22987–22994, https://doi.org/10.1029/93JD02553.

Schaaf, C. B., and Coauthors, 2002: First operational BRDF, albedo nadir reflectance products from MODIS. *Remote Sens. Environ.*, **83**, 135–148, https://doi.org/10.1016/S0034-4257(02)00091-3.

Schwartz, M. D., T. R. Ault, and J. L. Betancourt, 2013: Spring onset variations and trends in the continental United States: Past and regional assessment using temperature-based indices. *Int. J. Climatol.*, **33**, 2917–2922, https://doi.org/10.1002/joc.3625.

Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall's tau. *J. Amer. Stat. Assoc.*, **63**, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934.

Seneviratne, S. I., T. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling, 2010: Investigating soil moisture–climate interactions in a changing climate: A review. *Earth-Sci. Rev.*, **99**, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004.

Seyednasrollah, B., A. M. Young, K. Hufkens, T. Milliman, M. A. Friedl, S. Frolking, and A. D. Richardson, 2019: Publisher correction: Tracking vegetation phenology across diverse biomes using version 2.0 of the PhenoCam dataset. *Sci. Data*, **6**, 261, https://doi.org/10.1038/s41597-019-0270-8.

Shao, X., S.-P. Ho, X. Jing, X. Zhou, Y. Chen, T.-C. Liu, B. Zhang, and J. Dong, 2023: Characterizing the tropospheric water vapor spatial variation and trend using 2007–2018 COSMIC radio occultation and ECMWF reanalysis data. *Atmos. Chem. Phys.*, **23**, 14187–14218, https://doi.org/10.5194/acp-23-14187-2023.

Sharma, S., and Coauthors, 2021: Loss of ice cover, shifting phenology, and more extreme events in Northern Hemisphere lakes. *J. Geophys. Res. Biogeosci.*, **126**, e2021JG006348, https://doi.org/10.1029/2021JG006348.

—, and Coauthors, 2022: Author correction: Long-term ice phenology records spanning up to 578 years for 78 lakes around the Northern Hemisphere. *Sci. Data*, **9**, 419, https://doi.org/10.1038/s41597-022-01531-y.

Shi, L., and J. J. Bates, 2011: Three decades of intersatellite-calibrated High-Resolution Infrared Radiation Sounder upper tropospheric water vapor. *J. Geophys. Res.*, **116**, D04108, https://doi.org/10.1029/2010JD014847.

Simmons, A. J., 2022: Trends in the tropospheric general circulation from 1979 to 2022. *Wea. Climate Dyn.*, **3**, 777–809, https://doi.org/10.5194/wcd-3-777-2022.

Simpson, I. R., K. A. McKinnon, D. Kennedy, D. M. Lawrence, F. Lehner, and R. Seager, 2024: Observed humidity trends in dry regions contradict climate models. *Proc. Natl. Acad. Sci. USA*, **121**, e2302480120, https://doi.org/10.1073/pnas.2302480120.

Sindelarova, K., and Coauthors, 2014: Global data set of biogenic VOC emissions calculated by the MEGAN model over the last 30 years. *Atmos. Chem. Phys.*, **14**, 9317–9341, https://doi.org/10.5194/acp-14-9317-2014.

Smith, S. L., H. B. O'Neill, K. Isaksen, J. Noetzli, and V. E. Romanovsky, 2022: The changing thermal state of permafrost. *Nat. Rev. Earth Environ.*, **3**, 10–23, https://doi.org/10.1038/s43017-021-00240-1.

—, V. E. Romanovsky, K. Isaksen, K. E. Nyland, N. I. Shiklomanov, D. A. Streletskiy, and H. H. Christiansen, 2024: Permafrost [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), 314–317, https://doi.org/10.1175/BAMS-D-24-0116.1.

Soci, C., and Coauthors, 2024: The ERA5 global reanalysis from 1940 to 2022. *Quart. J. Roy. Meteor. Soc.*, **150**, 4014–4048, https://doi.org/10.1002/qj.4803.

Sofen, E. D., D. Bowdalo, and M. J. Evans, 2016: How to most effectively expand the global surface ozone observing network. *Atmos. Chem. Phys.*, **16**, 1445–1457, https://doi.org/10.5194/acp-16-1445-2016.

Solomon, S., K. Stone, P. Yu, D. M. Murphy, D. Kinnison, A. R. Ravishankara, and P. Wang, 2023: Chlorine activation and enhanced ozone depletion induced by wildfire aerosol. *Nature*, **615**, 259–264, https://doi.org/10.1038/s41586-022-05683-0.

Song, F., G. J. Zhang, V. Ramanathan, and L. R. Leung, 2022: Trends in surface equivalent potential temperature: A more comprehensive metric for global warming and weather extremes. *Proc. Natl. Acad. Sci. USA*, **119**, e2117832119, https://doi.org/10.1073/pnas.2117832119.

Song, X.-P., M. C. Hansen, S. V. Stehman, P. V. Potapov, A. Tyukavina, E. F. Vermote, and J. R. Townshend, 2018: Global land change from 1982 to 2016. *Nature*, **560**, 639–643, https://doi.org/10.1038/s41586-018-0411-9.

Sorg, A., A. Kääb, A. Roesch, C. Bigler, and M. Stoffel, 2015: Contrasting responses of central Asian rock glaciers to global warming. *Sci. Rep.*, **5**, 8228, https://doi.org/10.1038/srep08228.

South African Weather Service, 2024: Cape Town's record-breaking July 2024 rainfall. Document Reference: CS-CMS-LETT-003, accessed 12 August 2024, https://www.suidkaapforum.com/News/Article/National-News/cape-town-s-record-breaking-july-2024-rainfall-202408120436.

SPARC/IO3C/GAW, 2019: SPARC/IO3C/GAW report on long-term ozone trends and uncertainties in the stratosphere. I. Petropavlovskikh et al., Eds., SPARC Rep. 9, WCRP-17/2018, GAW Rep. 241, 102 pp., https://doi.org/10.17874/f899e-57a20b.
Spencer, R. W., J. R. Christy, and W. D. Braswell, 2017: UAH version 6 global satellite temperature products: Methodology and results. *Asia-Pac. J. Atmos. Sci.*, **53**, 121–130, https://doi.org/10.1007/s13143-017-0010-y.

Stackhouse, P. W., T. Wong, D. P. Kratz, P. Sawaengphokhai, A. C. Wiber, S. K. Gupta, and N. G. Loeb, 2016: Earth radiation budget at top-of-atmosphere [in “State of the Climate in 2015”]. *Bull. Amer. Meteor. Soc.*, **97** (8), S41–S43, https://doi.org/10.1175/2017BAMSstateoftheClimate.1.

Staub, B., C. Lambiel, and R. Delaloye, 2016: Rock glacier creep as a thermally-driven phenomenon: A decade of inter-annual observation from the Swiss Alps. *XI Int. Conf. on Permafrost*, Potsdam, Germany, Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, 96–97, https://doi.org/10.2312/GFZ.LIS.2016.001.

Stein, A. F., R. R. Draxler, G. D. Rolph, B. J. B. Stunder, M. D. Cohen, and F. Ngan, 2015: NOAA's HYSPLIT atmospheric transport and dispersion modeling system. *Bull. Amer. Meteor. Soc.*, **96**, 2059–2077, https://doi.org/10.1175/BAMS-D-14-00110.1.

Steinbrecht, W., and Coauthors, 2017: An update on ozone profile trends for the period 2000 to 2016. *Atmos. Chem. Phys.*, **17**, 10675–10690, https://doi.org/10.5194/acp-17-10675-2017.

Steiner, A. K., and Coauthors, 2020: Observed temperature changes in the troposphere and stratosphere from 1979 to 2018. *J. Climate*, **33**, 8165–8194, https://doi.org/10.1175/JCLI-D-19-0998.1.

Stocker, M., F. Ladstädter, and A. K. Steiner, 2021: Observing the climate impact of large wildfires on stratospheric temperature. *Sci. Rep.*, **11**, 22994, https://doi.org/10.1038/s41598-021-02335-7.

—, A. K. Steiner, F. Ladstädter, U. Foelsche, and W. J. Randel, 2024: Observed impacts of the Hunga Tonga eruption on stratospheric temperature. *EGU General Assembly 2024*, Vienna, Austria, EGU, EGU24-11683, https://doi.org/10.5194/egusphere-egu24-11683.

Stoy, P. C., J. Roh, and G. T. Bromley, 2022: It's the heat and the humidity: The complementary roles of temperature and specific humidity to recent changes in the energy content of the near-surface atmosphere. *Geophys. Res. Lett.*, **49**, e2021GL096628, https://doi.org/10.1029/2021GL096628.

Streletskiy, D. A., and Coauthors, 2025: Thawing permafrost is subsiding in the Northern Hemisphere—Review and perspectives. *Environ. Res. Lett.*, **20**, 013006, https://doi.org/10.1088/1748-9326/ada2ff.

Susskind, J., G. Molnar, L. Iredell, and N. G. Loeb, 2012: Interannual variability of outgoing longwave radiation as observed by AIRS and CERES. *J. Geophys. Res.*, **117**, D23107, https://doi.org/10.1029/2012JD017997.

Szopa, S., and Coauthors, 2021: Short-lived climate forcers. *Climate Change 2021: The Physical Science Basis*, V. Masson-Delmotte et al., Eds., Cambridge University Press, 817–922, https://doi.org/10.1017/9781009157896.008.

Taha, G., R. Loughman, P. R. Colarco, T. Zhu, L. W. Thomason, and G. Jaross, 2022: Tracking the 2022 Hunga Tonga-Hunga Ha'apai aerosol cloud in the upper and Middle stratosphere using space-based observations. *Geophys. Res. Lett.*, **49**, e2022GL100091, https://doi.org/10.1029/2022GL100091.

Tandon, A., 2024: Record-breaking Philippines typhoon season was 'supercharged' by climate change. Carbon Brief, https://www.carbonbrief.org/record-breaking-philippines-typhoon-season-was-supercharged-by-climate-change/.

Tapley, B. D., S. Bettadpur, J. C. Ries, P. F. Thompson, and M. M. Watkins, 2004: GRACE measurements of mass variability in the Earth system. *Science*, **305**, 503–505, https://doi.org/10.1126/science.1099192.

Tarasick, D. W., and Coauthors, 2019: Tropospheric ozone assessment report: Tropospheric ozone from 1877 to 2016, observed levels, trends and uncertainties. *Elementa*, **7**, 39, https://doi.org/10.1525/elementa.376.

The GlaMBIE Team, 2025: Community estimate of global glacier mass changes from 2000 to 2023. *Nature*, **639**, 382–388, https://doi.org/10.1038/s41586-024-08545-z.

Thibert, E., and X. Bodin, 2022: Changes in surface velocities over four decades on the Laurichard rock glacier (French Alps). *Permafrost Periglacial Processes*, **33**, 323–335, https://doi.org/10.1002/ppp.2159.

Thiery, W., E. Davin, S. Seneviratne, K. Bedka, S. Lhermitte, and N. van Lipzig, 2016: Hazardous thunderstorm intensification over Lake Victoria. *Nat. Commun.*, **7**, 12786, https://doi.org/10.1038/ncomms12786.

Thomason, L. W., and Coauthors, 2018: A global space-based stratospheric aerosol climatology: 1979–2016. *Earth Syst. Sci. Data*, **10**, 469–492, https://doi.org/10.5194/essd-10-469-2018.

Tian, H., and Coauthors, 2024: Global nitrous oxide budget 1980–2020. *Earth Syst. Sci. Data*, **16**, 2543–2604, https://doi.org/10.5194/essd-16-2543-2024.

Todt, M. A., E. Asher, E. Hall, P. Cullis, A. Jordan, K. Xiong, D. F. Hurst, and T. Thornberry, 2023: Baseline balloon stratospheric aerosol profiles (B2SAP)—Systematic measurements of aerosol number density and size. *J. Geophys. Res. Atmos.*, **128**, e2022JD038041, https://doi.org/10.1029/2022JD038041.

Toreti, A., and Coauthors, 2024a: Global drought overview September 2024 – GDO analytical report. Publications Office of the European Union Rep. JRC139423, 36 pp., https://data.europa.eu/doi/10.2760/7511271.

—, and Coauthors, 2024b: Drought in Europe - July 2024: GDO analytical report. 24 pp., https://doi.org/10.2760/420832.

—, and Coauthors, 2024c: Drought in the Amazon and the La Plata basins - December 2024: GDO analytical report. 30 pp., https://doi.org/10.2760/9524487.

Torralba, V., F. J. Doblas-Reyes, and N. Gonzalez-Reviriegol, 2017: Uncertainty in recent near-surface wind speed trends: A global reanalysis intercomparison. *Environ. Res. Lett.*, **12**, 114019, https://doi.org/10.1088/1748-9326/aa8a58.

Trenberth, K. E., 1997: The definition of El Niño. *Bull. Amer. Meteor. Soc.*, **78**, 2771–2777, https://doi.org/10.1175/1520-0477 (1997)078<2771:TDOENO>2.0.CO;2.

—, and J. T. Fasullo, 2013: Regional energy and water cycles: Transports from ocean to land. *J. Climate*, **26**, 7837–7851, https://doi.org/10.1175/JCLI-D-13-00008.1.

UNEP, 2024: Report of the Scientific Assessment Panel in response to Decision XXXV/7: Emissions of HFC-23. 35 pp., https://ozone.unep.org/system/files/documents/SAP_Report_on_HFC23_September2024.pdf.

United Nations, 2016: Paris Agreement to the United Nations Framework Convention on Climate Change, Dec. 12, 2015, T.I.A.S. No. 16-1104. 54 pp., https://unfccc.int/sites/default/files/resource/parisagreement_publication.pdf.

UNOCHA, 2024: Southern Africa: El Niño regional humanitarian overview. OCHA, https://www.unocha.org/publications/report/mozambique/southern-africa-el-nino-regional-humanitarian-overview-september-2024.
USDA and USDC, 2024: Weekly Weather and Crop Bulletin. Vol. 111, No. 11, 28 pp., https://downloads.usda.library.cornell.edu/usda-esmis/files/cj82k728n/m326np628/pk02f026h/wwcb1124.pdf.

van der Schalie, R., and Coauthors, 2017: The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E. Remote Sens. Environ., 189, 180–193, https://doi.org/10.1016/j.rse.2016.11.026.

van der Schrier, G., J. Barichivich, K. R. Briffa, and P. D. Jones, 2013: A scPDSI-based global dataset of dry and wet spells for 1901–2009. J. Geophys. Res. Atmos., 118, 4025–4048, https://doi.org/10.1002/jgrd.50355.

van der Werf, G. R., and Coauthors, 2017: Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data, 9, 697–720, https://doi.org/10.5194/essd-9-697-2017.

Van Dijk, A. I. J. M., and Coauthors, 2025: Global Water Monitor 2024, Summary Report. Global Water Monitor Consortium, 58 pp., https://www.globalwater.online/globalwater/report/.

Virts, K. S., and S. J. Goodman, 2020: Prolific lightning and thunderstorm initiation over the Lake Victoria basin in East Africa. Mon. Wea. Rev., 148, 1971–1985, https://doi.org/10.1175/MWR-D-19-0260.1.

—, J. M. Wallace, M. L. Hutchins, and R. H. Holzworth, 2015: Diurnal and seasonal lightning variability over the Gulf Stream and the Gulf of Mexico. J. Atmos. Sci., 72, 2657–2665, https://doi.org/10.1175/JAS-D-14-0233.1.

Vivero, S., X. Bodin, D. Farías-Barahona, S. MacDonell, N. Schaffer, B. A. Robson, and C. Lambiel, 2021: Combination of aerial, satellite, and UAV photogrammetry for quantifying rock glacier kinematics in the Dry Andes of Chile (30°S) since the 1950s. Front. Remote Sens., 2, 784015, https://doi.org/10.3389/frsen.2021.784015.

Vömel, H., S. Evan, and M. Tully, 2022: Water vapor injection into the stratosphere by Hunga Tonga-Hunga Haʻapai. Science, 377, 1444–1447, https://doi.org/10.1126/science.abq2299.

von Schuckmann, K., and Coauthors, 2023: Heat stored in the earth system 1960–2020: Where does the energy go? Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023.

Vose, R. S., and Coauthors, 2021: Implementing full spatial coverage in NOAA's global temperature analysis. Geophys. Res. Lett., 48, e2020GL090873, https://doi.org/10.1029/2020GL090873.

Vreugdenhil, M., and Coauthors, 2022: Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges. Front. Water, 4, 1045451, https://doi.org/10.3389/frwa.2022.1045451.

Wang, L., and Coauthors, 2023: Quantification of water released by thawing permafrost in the source region of the Yangtze River on the Tibetan Plateau by InSAR monitoring. Water Resour. Res., 59, e2023WR034451, https://doi.org/10.1029/2023WR034451.

Weatherhead, E. C., and Coauthors, 1998: Factors affecting the detection of trends: Statistical considerations and applications to environmental data. J. Geophys. Res., 103, 17149–17161, https://doi.org/10.1029/98JD00995.

Wells, N., S. Goddard, and M. J. Hayes, 2004: A self-calibrating palmer drought severity index. J. Climate, 17, 2335–2351, https://doi.org/10.1175/1520-0442(2004)017<2335:ASPSI>2.0.CO;2.

Wentz, F. J., 1997: A well-calibrated ocean algorithm for special sensor microwave/imager. J. Geophys. Res., 102, 8703–8718, https://doi.org/10.1029/96JC01751.

—, 2015: A 17-yr climate record of environmental parameters derived from the Tropical Rainfall Measuring Mission (TRMM) microwave imager. J. Climate, 28, 6882–6902, https://doi.org/10.1175/JCLI-D-15-0155.1.

—, L. Ricciardulli, K. Hilburn, and C. Mears, 2007: How much more rain will global warming bring? Science, 317, 233–235, https://doi.org/10.1126/science.1140746.

Wentz, K., T. Meissner, F. Wentz, and A. Manaster, 2024: Absolute intercalibration of spaceborne microwave radiometers. J. Atmos. Oceanic Technol., 41, 1121–1138, https://doi.org/10.1175/JTECH-D-24-0024.1.

Western, L. M., and Coauthors, 2023: Author correction: Global increase of ozone-depleting chlorofluorocarbons from 2010 to 2020. Nat. Geosci., 16, 546, https://doi.org/10.1038/s41561-023-01205-3.

—, and Coauthors, 2024: A decrease in radiative forcing and equivalent effective chlorine from hydrochlorofluorocarbons. Nat. Climate Change, 14, 805–807, https://doi.org/10.1038/s41558-024-02038-7.

Weyhenmeyer, G. A., and Coauthors, 2024: Global lake health in the anthropocene: Societal implications and treatment strategies. Earth's Future, 12, e2023EF004387, https://doi.org/10.1029/2023EF004387.

WFP, 2024: Eastern Africa Seasonal Monitor: Severe flooding across equatorial areas, April–May 2024. ReliefWeb, 15 pp., https://reliefweb.int/report/kenya/eastern-africa-seasonal-monitor-severe-flooding-across-equatorial-areas-april-may-2024.

WGMS, 2023: Global Glacier Change Bulletin. No. 5 (2020–2021), ISC(WDS)/IUGG(IACS)/UNEP/UNESCO/WMO, World Glacier Monitoring Service, 148 pp., https://doi.org/10.5904/wgms-fog-2023-09.

Wielicki, B. A., B. R. Barkstrom, E. F. Harrison, R. B. Lee, I. I. I. G. L. Smith, and J. E. Cooper, 1996: Clouds and the Earth's Radiant Energy System (CERES): An Earth observing system experiment. Bull. Amer. Meteor. Soc., 77, 853–868, https://doi.org/10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2.

—, and Coauthors, 1998: Clouds and the Earth's Radiant Energy System (CERES): Algorithm overview. IEEE Trans. Geosci. Remote Sens., 36, 1127–1141, https://doi.org/10.1109/36.701020.

Wiese, D. N., F. W. Landerer, and M. M. Watkins, 2016: Quantifying and reducing leakage errors in the JPL RL05M GRACE Mass solution. Water Resour. Res., 52, 7490–7502, https://doi.org/10.1002/2016WR019344.

Willett, K. M., 2023a: HadISDH/extremes Part I: A gridded wet bulb temperature extremes index product for climate monitoring. Adv. Atmos. Sci., 40, 1952–1967, https://doi.org/10.1007/s00376-023-2347-8.

—, 2023b: HadISDH/extremes Part II: Exploring humid heat extremes using wet bulb temperature indices. Adv. Atmos. Sci., 40, 1968–1985, https://doi.org/10.1007/s00376-023-2348-7.

—, 2023c: Sidebar 2.1: Assessing humid heat extremes over land [in "State of the Climate in 2022"). Bull. Amer. Meteor. Soc., 104 (9), S51–S53, https://doi.org/10.1175/BAMS-D-23-0090.1.

—, C. N. Williams Jr., R. J. H. Dunn, P. W. Thorne, S. Bell, M. de Podesta, P. D. Jones, and D. E. Parker, 2013: HadISDH: An updateable land surface specific humidity product for climate monitoring. Climate Past, 9, 657–677, https://doi.org/10.5194/cp-9-657-2013.

—, R. J. H. Dunn, P. W. Thorne, S. Bell, M. de Podesta, D. E. Parker, P. D. Jones, and C. N. Williams Jr., 2014: HadISDH land surface multi-variable humidity and temperature record for climate monitoring. Climate Past, 10, 1983–2006, https://doi.org/10.5194/cp-10-1983-2014.
—, —, J. Kennedy, and D. Berry, 2020: Development of the HadISDH.marine humidity climate monitoring dataset. *Earth Syst. Sci. Data*, **12**, 2853–2880, https://doi.org/10.5194/essd-12-2853-2020.

—, R. M. Horton, Y. T. E. Lo, C. Raymond, and C. D. W. Rogers, 2024: Humid-heat extremes over land [in "State of the Climate in 2023"). *Bull. Amer. Meteor. Soc.*, **105** (8), S55–S56, https://doi.org/10.1175/2024BAMSStateoftheClimate.1.

WMO, 2022: Scientific Assessment of Ozone Depletion: 2022. WMO Ozone Research and Monitoring GAW Rep. 278, 520 pp., https://ozone.unep.org/sites/default/files/2023-02/Scientific-Assessment-of-Ozone-Depletion-2022.pdf.

—, 2024: State of the Climate 2024. Update for COP29. WMO, 12 pp., https://library.wmo.int/idurl/4/69075.

—, 2025a: State of the Global Climate 2024. WMO-1368, 42 pp., https://library.wmo.int/records/item/69455-state-of-the-global-climate-2024.

—, 2025b: State of the Climate 2024. Significant Weather & Climate Events. https://wmo.int/sites/default/files/2025-03/State%20of%20the%20Global%20Climate%202024_Ex-tremes%20Supplement.pdf

Wohland, J., N.-E. Omrani, D. Witthaut, and N.-S. Keenlyside, 2019: Inconsistent wind speed trends in current twentieth century re-analyses. *J. Geophys. Res. Atmos.*, **124**, 1931–1940, https://doi.org/10.1029/2018JD030083.

Woolway, R. I., and C. J. Merchant, 2018: Intralake heterogeneity of thermal responses to climate change: A study of large Northern Hemisphere lakes. *J. Geophys. Res. Atmos.*, **123**, 3087–3098, https://doi.org/10.1002/2017JD027661.

—, and Coauthors, 2017: Lake surface temperature [in "State of the Climate in 2016"). *Bull. Amer. Meteor. Soc.*, **98** (8), S13–S14, https://doi.org/10.1175/2017BAMSStateoftheClimate.1.

—, and Coauthors, 2018: Lake surface temperature [in "State of the Climate in 2017"). *Bull. Amer. Meteor. Soc.*, **99** (8), S13–S15, https://doi.org/10.1175/2018BAMSStateoftheClimate.1.

WWA, 2024a: Climate change increased Typhoon Gaemi's wind speeds and rainfall, with devastating impacts across the western Pacific region. WWA, 58 pp., https://www.worldweather-attribution.org/climate-change-increased-typhoon-gaemis-wind-speeds-and-rainfall/.

—, 2024b: Rapid urbanisation and climate change key drivers of dramatic flood impacts in Nepal. WWA, 30 pp., https://www.worldweatherattribution.org/rapid-urbanisation-and-climate-change-key-drivers-of-dramatic-flood-impacts-in-nepal/.

Xing, J. W., and M. M. Wang, 2023: Trend and drivers of satellite-detected burned area changes across Arctic region since the 21st century. *J. Geophys. Res. Atmos.*, 128, https://doi.org/10.1029/2023JD038946.

Xu, H., L. Zhang, Z. Jin, A. Wang, Z. Liu, and F. Wang, 2025: Physiological strain under different wet bulb temperatures during day-long humid heat exposure in young men. *Build. Environ.*, **274**, 112783, https://doi.org/10.1016/j.buildenv.2025.112783.

Xu, N., H. Lu, W. Li, and P. Gong, 2024: Natural Lakes dominate global water storage variability. *Sci. Bull.*, **69**, 1016–1019, https://doi.org/10.1016/j.scib.2024.02.023.

Yang, Y., and Coauthors, 2023: Evapotranspiration on a green-ing earth. *Nat. Rev. Earth Environ.*, **4**, 626–641, https://doi.org/10.1038/s43017-023-00464-3.

Yao, F., B. Livneh, B. Rajagopalan, J. Wang, J.-F. Crétaux, Y. Wada, and M. Berge-Nguyen, 2023: Satellites reveal widespread decline in global lake water storage. *Science*, **380**, 743–749, https://doi.org/10.1126/science.abo2812.

Yeh, S., and Coauthors, 2018: ENSO atmospheric teleconnections and their response to greenhouse gas forcing. *Rev. Geophys.*, **56**, 185–206, https://doi.org/10.1002/2017RG000568.

Yoon, J., and S.-W. Yeh, 2010: Influence of the Pacific decadal oscillation on the relationship between El Niño and the northeast Asian summer monsoon. *J. Climate*, **23**, 4525–4537, https://doi.org/10.1175/2010JCLI3352.1.

Yosef, Y., E. Aguilar, and P. Alpert, 2021: Is it possible to fit extreme climate change indices together seamlessly in the era of accelerated warming? *Int. J. Climatol.*, **41**, E952–E963, https://doi.org/10.1002/joc.6740.

Young, S. S., 2023: Global and regional snow cover decline: 2000–2022. *Climate*, **11**, 162, https://doi.org/10.3390/cli11080162.

Yu, P., and Coauthors, 2021: Persistent stratospheric warming due to 2019–2020 Australian wildfire smoke. *Geophys. Res. Lett.*, **48**, e2021GL092609, https://doi.org/10.1029/2021GL092609.

Zeng, Z., and Coauthors, 2019: A reversal in global terrestrial stilling and its implications for wind energy production. *Nat. Climate Change*, **9**, 979–985, https://doi.org/10.1038/s41558-019-0622-6.

Zha, J., and Coauthors, 2021: Projected changes in global terrestrial near-surface wind speed in 1.5°C–4.0°C global warming levels. *Environ. Res. Lett.*, **16**, 114016, https://doi.org/10.1088/1748-9326/ac2fdd.

Zhang, G. F., and Coauthors, 2021: Uneven warming likely contributed to declining near-surface wind speeds in northern China between 1961 and 2016. *J. Geophys. Res. Atmos.*, **126**, e2020JD033637, https://doi.org/10.1029/2020JD033637.

Zhang, S., S. Solomon, C. D. Boone, and G. Taha, 2024: Investigating the vertical extent of the 2023 summer Canadian wildfire impacts with satellite observations. *Atmos. Chem. Phys.*, **24**, 11727–11736, https://doi.org/10.5194/acp-24-11727-2024.

Zhang, W., and Coauthors, 2025: A year marked by extreme precipitation and floods: Weather and climate extremes in 2024. *Adv. Atmos. Sci.*, **42**, 1045–1063, https://doi.org/10.1007/s00376-025-4540-4.

Zhang, X., L. Alexander, G. C. Hegerl, P. Jones, A. K. Tank, T. C. Peterson, B. Trewin, and F. W. Zwiers, 2011: Indices for monitoring changes in extremes based on daily temperature and precipitation data. *Wiley Interdiscip. Rev.: Climate Change*, **2**, 851–870, https://doi.org/10.1002/wcc.147.

Zhang, Z., B. Poulter, A. F. Feldman, Q. Ying, P. Ciais, S. Peng, and X. Li, 2023: Recent intensification of wetland methane feedback. *Nat. Climate Change*, **13**, 430–433, https://doi.org/10.1038/s41558-023-01629-0.

Zhao, L., and Coauthors, 2024: Investigation, monitoring, and simulation of permafrost on the Qinghai–Tibet Plateau: A review. *Permafrost Periglacial Processes*, **35**, 412–422, https://doi.org/10.1002/ppp.2227.

Zhao, Y., and Coauthors, 2019: Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period. *Atmos. Chem. Phys.*, **19**, 13701–13723, https://doi.org/10.5194/acp-19-13701-2019.

Zhao, Y., H. Norouzi, M. Azarderakhsh, and A. AghaKouchak, 2021: Global patterns of hottest, coldest, and extreme diurnal variability on earth. *Bull. Amer. Meteor. Soc.*, **102**, E1672–E1681, https://doi.org/10.1175/BAMS-D-20-0325.1.

Zheng, B., and Coauthors, 2023: Record-high CO₂ emissions from boreal fires in 2021. *Science*, **379**, 912–917, https://doi.org/10.1126/science.ade0805.
Zhou, X., and Coauthors, 2024: Antarctic vortex dehydration in 2023 as a substantial removal pathway for Hunga Tonga-Hunga Ha'apai water vapor. *Geophys. Res. Lett.*, **51**, e2023GL107630, https://doi.org/10.1029/2023GL107630.

Zhu, Z., and Coauthors, 2016: Greening of the Earth and its drivers. *Nat. Climate Change*, **6**, 791–795, https://doi.org/10.1038/nclimate3004.

Ziemke, J. R., and Coauthors, 2019: Trends in global tropospheric ozone inferred from a composite record of TOMS/OMI/MLS/OMPS satellite measurements and the MERRA-2 GMI simulation. *Atmos. Chem. Phys.*, **19**, 3257–3269, https://doi.org/10.5194/acp-19-3257-2019.

—, and Coauthors, 2022: NASA satellite measurements show global-scale reductions in free tropospheric ozone in 2020 and again in 2021 during COVID-19. *Geophys. Res. Lett.*, **49**, e2022GL098712, https://doi.org/10.1029/2022GL098712.

Ziese, M., A. Rauthe-Schöch, S. Hänsel, P. Finger, E. Rustemeier, and U. Schneider, 2022: GPCC full data daily version 2022 at 1.0°: Daily Land-Surface precipitation from Rain-Gauges built on GTS-based and historic data: Globally gridded daily totals (version 2022, p. approx. 25 MB per gzip file) [NetCDF]. GPCC, https://doi.org/10.5676/DWD GPCC/FD_D_V2022_100.

Zolghadrshojaee, M., S. Tegtmeier, S. M. Davis, and R. Pilch Kedzierski, 2024: Variability and long-term changes in tropical cold-point temperature and water vapor. *Atmos. Chem. Phys.*, **24**, 7405–7419, https://doi.org/10.5194/acp-24-7405-2024.

Zotta, R. M., R. van der Schalie, W. Preimesberger, R. De Jeu, and W. Dorigo, 2023: Vegetation optical depth [in “State of the Climate in 2023”]. *Bull. Amer. Meteor. Soc.*, **105** (8), S113–S114, https://doi.org/10.1175/BAMS-D-24-0116.1.

—, L. Moesinger, R. van der Schalie, M. Vreugdenhil, W. Preimesberger, T. Frederikse, R. De Jeu, and W. Dorigo, 2024a: VODCA v2: Multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring (1.0.0). TU Wien, accessed 16 January 2025, https://doi.org/10.48436/t74ty-tcx62.

—, —, —, —, —, —, —, 2024b: VODCA v2: Multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring. *Earth Syst. Sci. Data*, **16**, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024.

Zou, C.-Z., H. Xu, X. Hao, and Q. Liu, 2023: Mid-tropospheric layer temperature record derived from satellite microwave observations with backward merging approach. *J. Geophys. Res. Atmos.*, **128**, e2022JD037472, https://doi.org/10.1029/2022JD037472.

Zou, D., and Coauthors, 2024: Estimation of permafrost ground ice to 10-m depth on the Qinghai–Tibet Plateau. *Permafrost Periglacial Processes*, **35**, 423–434, https://doi.org/10.1002/ppp.2226]