#climatesciences — Public Fediverse posts

Field-space autoencoder for scalable climate emulators

Data We use the equal-area HEALPix grid for all of our models. We index HEALPix resolution by a…
#Climate #ClimateChange #Climate-Change #artificialintelligence #climatechange #Climatesciences #globalwarming #Mathematicsandcomputing
https://www.europesays.com/2992996/

East Asian winter monsoon reshapes aerosol composition and CCN activation over the China Seas https://www.byteseu.com/2022530/ #AtmosphericProtection/AirQualityControl/AirPollution #AtmosphericSciences #Climate #ClimateChange #ClimateChange/ClimateChangeImpacts #ClimateSciences #Climatology #EarthSciences #EnvironmentalSciences #General #GlobalWarming

https://www.europesays.com/uk/956486/ Ice core reveals longest-ever continuous record of Earth’s climate #ClimateChange #ClimateSciences #HumanitiesAndSocialSciences #multidisciplinary #Science #UK #UnitedKingdom

Ice core reveals longest-ever continuous record of Earth’s climate

Antarctic ice cores preserve tiny bubbles of ancient air, offering a record of Earth’s past atmosphere.Credit: British Antarctic…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Science #climatechange #Climatesciences #HumanitiesandSocialSciences #multidisciplinary
https://www.newsbeep.com/us/639918/

Ice core reveals longest-ever continuous record of Earth’s climate

Antarctic ice cores preserve tiny bubbles of ancient air, offering a record of Earth’s past atmosphere.Credit: British Antarctic…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Science #climatechange #Climatesciences #HumanitiesandSocialSciences #multidisciplinary
https://www.newsbeep.com/us/639918/

Ice core reveals longest-ever continuous record of Earth’s climate

Antarctic ice cores preserve tiny bubbles of ancient air, offering a record of Earth’s past atmosphere.Credit: British Antarctic…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Science #climatechange #Climatesciences #HumanitiesandSocialSciences #multidisciplinary
https://www.newsbeep.com/us/639918/

Ice core reveals longest-ever continuous record of Earth’s climate

Antarctic ice cores preserve tiny bubbles of ancient air, offering a record of Earth’s past atmosphere.Credit: British Antarctic…
#NewsBeep #News #Science #ClimateChange #Climatesciences #GB #HumanitiesandSocialSciences #multidisciplinary #UK #UnitedKingdom
https://www.newsbeep.com/uk/580970/

Ice core reveals longest-ever continuous record of Earth’s climate

Antarctic ice cores preserve tiny bubbles of ancient air, offering a record of Earth’s past atmosphere.Credit: British Antarctic…
#NewsBeep #News #Science #ClimateChange #Climatesciences #GB #HumanitiesandSocialSciences #multidisciplinary #UK #UnitedKingdom
https://www.newsbeep.com/uk/580970/

Climate governance overlooks the ocean: a structural limitation exposed at COP30 https://www.byteseu.com/2005723/ #Climate #ClimateChange #ClimateChangeManagementAndPolicy #ClimateChange/ClimateChangeImpacts #ClimateSciences #EnvironmentalSocialSciences #EnvironmentalStudies #Freshwater&MarineEcology #GlobalWarming #Marine&FreshwaterSciences #OceanSciences #oceanography #ScientificCommunity

Assessing urban thermal comfort: a multi-model analysis of European cities over two decades

This study examines changes in summer thermal comfort across 12 European cities in 2000, 2010, and 2020 by…
#Europe #EU #Cities #Climatesciences #Ecology #environment #Environmentalsciences #European #general #Geography #Sustainabledevelopment #towns) #UrbanEcology #UrbanGeography/Urbanism(inc.megacities #Urbanism
https://www.europesays.com/europe/35236/

https://www.europesays.com/africa/217507/ Clustering and machine learning techniques identify air pollution regimes in Greater Cairo #AirPollution #ClimateSciences #DecisionTrees #Egypt #EnvironmentalSciences #EnvironmentalSocialSciences #GreaterCairo #HumanitiesAndSocialSciences #KMeansClustering #MachineLearning #MathematicsAndComputing #multidisciplinary #RandomForest #science #UrbanEnvironments

https://www.europesays.com/ie/450660/ Remote sensing and process attribution uncertainties in the Dharali event #AtmosphericSciences #ClimateSciences #Éire #Environment #EnvironmentalPolicy #IE #Ireland #NaturalHazards #Science #SolidEarthSciences

Nuclear Conflict in Eastern Europe: Climate disruption and Radiological fallout

Ukraine–Russia conflict: year 1 after detonation After detonation, we explore the spatiotemporal evolution of the aerosol particles and…
#Europe #EU #Climatesciences #EnvironmentalHealth #Environmentalsciences
https://www.europesays.com/europe/20599/

Portfolio optimization for industrial cluster defossilization in the Port of Rotterdam

In this section, the proposed portfolio optimization model for the defined scenarios are applied and risk-return relationships are…
#Netherlands #Nederland #NL #Europe #Europa #EU #Rotterdam #Climatesciences #Energyscienceandtechnology #Environmentalsciences #Environmentalsocialsciences #HumanitiesandSocialSciences #multidisciplinary #Science
https://www.europesays.com/netherlands/1127/

https://www.europesays.com/dk/47182/ Entrained debris records regrowth of the Greenland Ice Sheet after the last interglacial #ClimateSciences #CryosphericScience #EarthSciences #EarthSystemSciences #General #Geochemistry #geology #Geophysics/Geodesy #Greenland

Broadly stable atmospheric CO2 and CH4 levels over the past 3 million years

Lüthi, D. et al. High-resolution carbon dioxide concentration record 650,000–800,000 years before present. Nature 453, 379–382 (2008). Article …
#NewsBeep #News #Science #Climatesciences #GB #HumanitiesandSocialSciences #multidisciplinary #Palaeoclimate #UK #UnitedKingdom
https://www.newsbeep.com/uk/483842/

https://www.europesays.com/uk/835970/ Broadly stable atmospheric CO2 and CH4 levels over the past 3 million years #ClimateSciences #HumanitiesAndSocialSciences #multidisciplinary #Palaeoclimate #Science #UK #UnitedKingdom

Broadly stable atmospheric CO2 and CH4 levels over the past 3 million years

Lüthi, D. et al. High-resolution carbon dioxide concentration record 650,000–800,000 years before present. Nature 453, 379–382 (2008). Article …
#NewsBeep #News #Science #AU #Australia #Climatesciences #HumanitiesandSocialSciences #multidisciplinary #Palaeoclimate
https://www.newsbeep.com/au/549387/

Broadly stable atmospheric CO2 and CH4 levels over the past 3 million years

Lüthi, D. et al. High-resolution carbon dioxide concentration record 650,000–800,000 years before present. Nature 453, 379–382 (2008). Article …
#NewsBeep #News #Science #AU #Australia #Climatesciences #HumanitiesandSocialSciences #multidisciplinary #Palaeoclimate
https://www.newsbeep.com/au/549387/

https://www.europesays.com/ie/389368/ Megafires in Mediterranean Europe: the compound role of fire weather and drought #AtmosphericSciences #ClimateSciences #Ecology #Éire #Environment #EnvironmentalPolicy #EnvironmentalSciences #IE #Ireland #NaturalHazards #Science

https://www.europesays.com/ie/271999/ Arctic driftwood proposal for durable carbon removal #ClimateChange #ClimateChangeManagementAndPolicy #ClimateSciences #Ecology #Éire #Environment #EnvironmentalEconomics #EnvironmentalPolitics #EnvironmentalSciences #IE #Ireland #OceanSciences #Science #SocialPolicy

Arctic driftwood proposal for durable carbon removal

Zeng, N. et al. Carbon sequestration via wood harvest and storage: an assessment of its harvest potential. Clim.…
#NewsBeep #News #Environment #ClimateChange #ClimateChangeManagementandPolicy #Climatesciences #Ecology #environment #Environmentaleconomics #EnvironmentalPolitics #Environmentalsciences #Oceansciences #Science #Socialpolicy #UK #UnitedKingdom
https://www.newsbeep.com/uk/356530/

https://www.europesays.com/ie/168299/ Integrating RUSLE, AHP, GIS, and cloud-based geospatial analysis for soil erosion assessment under mediterranean conditions #Algeria #ClimateSciences #Ecology #Éire #Environment #EnvironmentalSciences #GEE;AHP #HumanitiesAndSocialSciences #Hydrology #IE #Ireland #LandDegradation #MitidjaPlain #multidisciplinary #NaturalHazards #RUSLE #Science #SoilErosion

Perspective on the shifting interannual variability of recent summer temperature modes in eastern China: Roles of Arctic sea-ice, Arctic Oscillation and Pakistan precipitation https://www.byteseu.com/1520015/ #AtmosphericProtection/AirQualityControl/AirPollution #AtmosphericSciences #Climate #ClimateChange #ClimateChange/ClimateChangeImpacts #ClimateSciences #Climatology #EarthSciences #EnvironmentalSciences #General #GlobalWarming #OceanSciences

Strengthening India’s climate-health resilience: a public health imperative https://www.byteseu.com/1518159/ #ClimateChange #ClimateChangeManagementAndPolicy #ClimateSciences #EnvironmentalEconomics #EnvironmentalPolitics #EnvironmentalSciences #EnvironmentalSocialSciences #EnvironmentalStudies #Health #SocialPolicy

https://www.europesays.com/ie/146340/ Predicting water scarcity in northern Bangladesh using deep learning and climate data #AtmosphericProtection/AirQualityControl/AirPollution #AtmosphericSciences #ClimateChange/ClimateChangeImpacts #ClimateSciences #Climatology #EarthSciences #Éire #Environment #general #Hydrology #IE #Ireland #Science #WaterResources

Predicting water scarcity in northern Bangladesh using deep learning and climate data

Baseline Maps Figures 1 and 2a illustrate the area percentage of drought susceptibility classes across three agricultural periods:…
#NewsBeep #News #Environment #AtmosphericProtection/AirQualityControl/AirPollution #AtmosphericSciences #CA #Canada #ClimateChange/ClimateChangeImpacts #Climatesciences #Climatology #earthsciences #general #Hydrology #Science #Waterresources
https://www.newsbeep.com/ca/241525/

https://www.europesays.com/uk/528721/ Predicting water scarcity in northern Bangladesh using deep learning and climate data #AtmosphericProtection/AirQualityControl/AirPollution #AtmosphericSciences #ClimateChange/ClimateChangeImpacts #ClimateSciences #Climatology #EarthSciences #Environment #general #Hydrology #Science #UK #UnitedKingdom #WaterResources

Predicting water scarcity in northern Bangladesh using deep learning and climate data

Baseline Maps Figures 1 and 2a illustrate the area percentage of drought susceptibility classes acro…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Environment #AtmosphericProtection/AirQualityControl/AirPollution #AtmosphericSciences #ClimateChange/ClimateChangeImpacts #Climatesciences #Climatology #EarthSciences #general #hydrology #Science #Waterresources
https://www.newsbeep.com/us/252233/

Predicting water scarcity in northern Bangladesh using deep learning and climate data

Baseline Maps Figures 1 and 2a illustrate the area percentage of drought susceptibility classes acro…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Environment #AtmosphericProtection/AirQualityControl/AirPollution #AtmosphericSciences #ClimateChange/ClimateChangeImpacts #Climatesciences #Climatology #EarthSciences #general #hydrology #Science #Waterresources
https://www.newsbeep.com/us/252233/

Predicting water scarcity in northern Bangladesh using deep learning and climate data

Baseline Maps Figures 1 and 2a illustrate the area percentage of drought susceptibility classes across three agric…
#NewsBeep #News #Environment #AtmosphericProtection/AirQualityControl/AirPollution #AtmosphericSciences #ClimateChange/ClimateChangeImpacts #Climatesciences #Climatology #EarthSciences #environment #general #Hydrology #Science #UK #UnitedKingdom #Waterresources
https://www.newsbeep.com/uk/226040/

Vegetation cover change as a growing driver of global leaf area index dynamics

DatasetsVegetation cover data The Vegetation Continuous Fields version 1 product (VCF5KYR) was adopted to provide global fine-scale fractional…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Environment #Climatesciences #Ecosystemecology #Environmentalsciences #Forestry #HumanitiesandSocialSciences #multidisciplinary #Science
https://www.newsbeep.com/us/239767/

Basics of Numerical Weather Prediction (NWP):

1. THE HORIZONTAL MOMENTUM EQUATION:
\[
\frac{d\mathbf{V}}{dt} + f\hat{k} \times \mathbf{V} = -\nabla \phi + \frac{\sigma}{p_s} \frac{\partial \phi}{\partial \sigma} \nabla p_s + \mathbf{F}
\]

2. THE CONTINUITY EQUATION:
\[
\frac{\partial p_s}{\partial t} + \nabla \cdot (p_s \mathbf{V}) + \frac{\partial}{\partial \sigma}(p_s \dot{\sigma}) = 0
\]

3. THE THERMODYNAMIC ENERGY EQUATION:
\[
\frac{1}{R} \frac{d}{dt} \left[ \sigma \frac{\partial \phi}{\partial \sigma} \right] + \frac{RT}{C_p p} \left[ p_s \dot{\sigma} + \sigma\dot{p_s} \right] = -Q
\]

4. HYDROSTATIC EQUATION:
\[
\frac{\partial \phi}{\partial \sigma} = -\frac{RT_v}{\sigma}
\]

5. SURFACE PRESSURE TENDENCY EQUATION:
\[\displaystyle
\frac{\partial p_s}{\partial t} = -\int_{0}^{1} \nabla\cdot (p_s \mathbf{V}) \, d\sigma
\]

6. MOISTURE EQUATION:
\[\displaystyle
\frac{\partial}{\partial t} (p_s q) + \nabla\cdot (p_s q \mathbf{V}) + \frac{\partial}{\partial \sigma} (p_s q \dot{\sigma}) = p_s S
\]

The six primary unknowns are: \(\mathbf{V}\) (horizontal wind velocity), \(p_s\) (surface pressure), \(T\) (temperature), \(q\) (specific humidity or moisture), \(\phi\) (geopotential), and \(\dot{\sigma}\) (sigma velocity or vertical velocity in \(\sigma\)-coordinates).

#NWP #Weather #NumericalWeatherPrediction #Meteorology #Climate #ClimateScience #Earth #EarthScience #ClimateChange #ClimateSciences #Science #WeatherPrediction #Humidity #Moisture #Pressure #Velocity #SurfacePressure #HydrostaticEquation #WeatherPrediction #Ocean #Atmosphere #AOS #ClimateDynamics #WeatherDynamics #Geopotential #SigmaVelocity #VerticalVelocity #MoistureEquation #Thermodynamics #Dynamics #NavierStokes

Basics of Numerical Weather Prediction (NWP):

1. THE HORIZONTAL MOMENTUM EQUATION:
\[
\frac{d\mathbf{V}}{dt} + f\hat{k} \times \mathbf{V} = -\nabla \phi + \frac{\sigma}{p_s} \frac{\partial \phi}{\partial \sigma} \nabla p_s + \mathbf{F}
\]

2. THE CONTINUITY EQUATION:
\[
\frac{\partial p_s}{\partial t} + \nabla \cdot (p_s \mathbf{V}) + \frac{\partial}{\partial \sigma}(p_s \dot{\sigma}) = 0
\]

3. THE THERMODYNAMIC ENERGY EQUATION:
\[
\frac{1}{R} \frac{d}{dt} \left[ \sigma \frac{\partial \phi}{\partial \sigma} \right] + \frac{RT}{C_p p} \left[ p_s \dot{\sigma} + \sigma\dot{p_s} \right] = -Q
\]

4. HYDROSTATIC EQUATION:
\[
\frac{\partial \phi}{\partial \sigma} = -\frac{RT_v}{\sigma}
\]

5. SURFACE PRESSURE TENDENCY EQUATION:
\[\displaystyle
\frac{\partial p_s}{\partial t} = -\int_{0}^{1} \nabla\cdot (p_s \mathbf{V}) \, d\sigma
\]

6. MOISTURE EQUATION:
\[\displaystyle
\frac{\partial}{\partial t} (p_s q) + \nabla\cdot (p_s q \mathbf{V}) + \frac{\partial}{\partial \sigma} (p_s q \dot{\sigma}) = p_s S
\]

The six primary unknowns are: \(\mathbf{V}\) (horizontal wind velocity), \(p_s\) (surface pressure), \(T\) (temperature), \(q\) (specific humidity or moisture), \(\phi\) (geopotential), and \(\dot{\sigma}\) (sigma velocity or vertical velocity in \(\sigma\)-coordinates).

#NWP #Weather #NumericalWeatherPrediction #Meteorology #Climate #ClimateScience #Earth #EarthScience #ClimateChange #ClimateSciences #Science #WeatherPrediction #Humidity #Moisture #Pressure #Velocity #SurfacePressure #HydrostaticEquation #WeatherPrediction #Ocean #Atmosphere #AOS #ClimateDynamics #WeatherDynamics #Geopotential #SigmaVelocity #VerticalVelocity #MoistureEquation #Thermodynamics #Dynamics #NavierStokes

Basics of Numerical Weather Prediction (NWP):

1. THE HORIZONTAL MOMENTUM EQUATION:
\[
\frac{d\mathbf{V}}{dt} + f\hat{k} \times \mathbf{V} = -\nabla \phi + \frac{\sigma}{p_s} \frac{\partial \phi}{\partial \sigma} \nabla p_s + \mathbf{F}
\]

2. THE CONTINUITY EQUATION:
\[
\frac{\partial p_s}{\partial t} + \nabla \cdot (p_s \mathbf{V}) + \frac{\partial}{\partial \sigma}(p_s \dot{\sigma}) = 0
\]

3. THE THERMODYNAMIC ENERGY EQUATION:
\[
\frac{1}{R} \frac{d}{dt} \left[ \sigma \frac{\partial \phi}{\partial \sigma} \right] + \frac{RT}{C_p p} \left[ p_s \dot{\sigma} + \sigma\dot{p_s} \right] = -Q
\]

4. HYDROSTATIC EQUATION:
\[
\frac{\partial \phi}{\partial \sigma} = -\frac{RT_v}{\sigma}
\]

5. SURFACE PRESSURE TENDENCY EQUATION:
\[\displaystyle
\frac{\partial p_s}{\partial t} = -\int_{0}^{1} \nabla\cdot (p_s \mathbf{V}) \, d\sigma
\]

6. MOISTURE EQUATION:
\[\displaystyle
\frac{\partial}{\partial t} (p_s q) + \nabla\cdot (p_s q \mathbf{V}) + \frac{\partial}{\partial \sigma} (p_s q \dot{\sigma}) = p_s S
\]

The six primary unknowns are: \(\mathbf{V}\) (horizontal wind velocity), \(p_s\) (surface pressure), \(T\) (temperature), \(q\) (specific humidity or moisture), \(\phi\) (geopotential), and \(\dot{\sigma}\) (sigma velocity or vertical velocity in \(\sigma\)-coordinates).

#NWP #Weather #NumericalWeatherPrediction #Meteorology #Climate #ClimateScience #Earth #EarthScience #ClimateChange #ClimateSciences #Science #WeatherPrediction #Humidity #Moisture #Pressure #Velocity #SurfacePressure #HydrostaticEquation #WeatherPrediction #Ocean #Atmosphere #AOS #ClimateDynamics #WeatherDynamics #Geopotential #SigmaVelocity #VerticalVelocity #MoistureEquation #Thermodynamics #Dynamics #NavierStokes

Basics of Numerical Weather Prediction (NWP):

1. THE HORIZONTAL MOMENTUM EQUATION:
\[
\frac{d\mathbf{V}}{dt} + f\hat{k} \times \mathbf{V} = -\nabla \phi + \frac{\sigma}{p_s} \frac{\partial \phi}{\partial \sigma} \nabla p_s + \mathbf{F}
\]

2. THE CONTINUITY EQUATION:
\[
\frac{\partial p_s}{\partial t} + \nabla \cdot (p_s \mathbf{V}) + \frac{\partial}{\partial \sigma}(p_s \dot{\sigma}) = 0
\]

3. THE THERMODYNAMIC ENERGY EQUATION:
\[
\frac{1}{R} \frac{d}{dt} \left[ \sigma \frac{\partial \phi}{\partial \sigma} \right] + \frac{RT}{C_p p} \left[ p_s \dot{\sigma} + \sigma\dot{p_s} \right] = -Q
\]

4. HYDROSTATIC EQUATION:
\[
\frac{\partial \phi}{\partial \sigma} = -\frac{RT_v}{\sigma}
\]

5. SURFACE PRESSURE TENDENCY EQUATION:
\[\displaystyle
\frac{\partial p_s}{\partial t} = -\int_{0}^{1} \nabla\cdot (p_s \mathbf{V}) \, d\sigma
\]

6. MOISTURE EQUATION:
\[\displaystyle
\frac{\partial}{\partial t} (p_s q) + \nabla\cdot (p_s q \mathbf{V}) + \frac{\partial}{\partial \sigma} (p_s q \dot{\sigma}) = p_s S
\]

The six primary unknowns are: \(\mathbf{V}\) (horizontal wind velocity), \(p_s\) (surface pressure), \(T\) (temperature), \(q\) (specific humidity or moisture), \(\phi\) (geopotential), and \(\dot{\sigma}\) (sigma velocity or vertical velocity in \(\sigma\)-coordinates).

#NWP #Weather #NumericalWeatherPrediction #Meteorology #Climate #ClimateScience #Earth #EarthScience #ClimateChange #ClimateSciences #Science #WeatherPrediction #Humidity #Moisture #Pressure #Velocity #SurfacePressure #HydrostaticEquation #WeatherPrediction #Ocean #Atmosphere #AOS #ClimateDynamics #WeatherDynamics #Geopotential #SigmaVelocity #VerticalVelocity #MoistureEquation #Thermodynamics #Dynamics #NavierStokes

Basics of Numerical Weather Prediction (NWP):

1. THE HORIZONTAL MOMENTUM EQUATION:
\[
\frac{d\mathbf{V}}{dt} + f\hat{k} \times \mathbf{V} = -\nabla \phi + \frac{\sigma}{p_s} \frac{\partial \phi}{\partial \sigma} \nabla p_s + \mathbf{F}
\]

2. THE CONTINUITY EQUATION:
\[
\frac{\partial p_s}{\partial t} + \nabla \cdot (p_s \mathbf{V}) + \frac{\partial}{\partial \sigma}(p_s \dot{\sigma}) = 0
\]

3. THE THERMODYNAMIC ENERGY EQUATION:
\[
\frac{1}{R} \frac{d}{dt} \left[ \sigma \frac{\partial \phi}{\partial \sigma} \right] + \frac{RT}{C_p p} \left[ p_s \dot{\sigma} + \sigma\dot{p_s} \right] = -Q
\]

4. HYDROSTATIC EQUATION:
\[
\frac{\partial \phi}{\partial \sigma} = -\frac{RT_v}{\sigma}
\]

5. SURFACE PRESSURE TENDENCY EQUATION:
\[\displaystyle
\frac{\partial p_s}{\partial t} = -\int_{0}^{1} \nabla\cdot (p_s \mathbf{V}) \, d\sigma
\]

6. MOISTURE EQUATION:
\[\displaystyle
\frac{\partial}{\partial t} (p_s q) + \nabla\cdot (p_s q \mathbf{V}) + \frac{\partial}{\partial \sigma} (p_s q \dot{\sigma}) = p_s S
\]

The six primary unknowns are: \(\mathbf{V}\) (horizontal wind velocity), \(p_s\) (surface pressure), \(T\) (temperature), \(q\) (specific humidity or moisture), \(\phi\) (geopotential), and \(\dot{\sigma}\) (sigma velocity or vertical velocity in \(\sigma\)-coordinates).

#NWP #Weather #NumericalWeatherPrediction #Meteorology #Climate #ClimateScience #Earth #EarthScience #ClimateChange #ClimateSciences #Science #WeatherPrediction #Humidity #Moisture #Pressure #Velocity #SurfacePressure #HydrostaticEquation #WeatherPrediction #Ocean #Atmosphere #AOS #ClimateDynamics #WeatherDynamics #Geopotential #SigmaVelocity #VerticalVelocity #MoistureEquation #Thermodynamics #Dynamics #NavierStokes

Exploring vegetation health in Southern Thailand under climate stress from temperature and water impacts between 2000 and 2023 https://www.byteseu.com/1302594/ #Climate #ClimateChange #ClimateSciences #ClimateStress #DroughtImpacts #ecology #EcosystemResilience #EnvironmentalSciences #GlobalWarming #HumanitiesAndSocialSciences #kNDVI #LandSurfaceTemperature #multidisciplinary #PlanetaryScience #Science #SouthernThailand #SpatiotemporalAnalysis #VegetationHealthIndex

The answer seems to be:
1. assess and present risks to policymakers and the public
2. Use your voices and authority (climate scientists have more authority which means more power which means more responsibility..)

#Climate #ClimateSciences #ClimateActivists

https://fediscience.org/@Ruth_Mottram/111421107786654925