10/ 🌴 Read the full review in One Earth to learn more about actionable solutions and research priorities for supporting tropical outdoor workers in a warming world. Let's work together for a more resilient future. 🔗 doi.org/10.1016/j.on... (10/10)

9/ 🚀 The path forward: Immediate adaptation is critical, but the ultimate solution lies in cutting emissions and preserving tropical forests or other low-cost cooling strategies. Without action, the safety and livelihoods of millions remain at risk. (9/10)

8/ 🧪 Research gaps remain: How do we adapt to compounded stressors like heat + air pollution? What are the unintended consequences of adaptation efforts? Addressing these will refine our strategies for building resilience. (8/10)

7/ 🌟 Governments, employers, and communities each have roles:

Enact labor protections
Promote education on heat risks
Provide adaptive tools like flexible work hours, lightweight clothing, and cooling tech
Develop sustainable livelihoods (7/10)

6/ 💡 To protect workers, we need layered solutions:

🌳 Primary: Reduce heat exposure (emissions cuts, reforestation, and urban greening)
⏳ Secondary: Mitigate risks during heat events (e.g., rest breaks, cooling stations).
🏥 Tertiary: Provide recovery support (medical care and financial aid). (6/10)

5/ 🚶‍♀️ Individual workers are adapting—changing work hours, hydrating, or seeking shade—but these solutions have limits. Chronic heat exposure leads to long-term health issues like kidney disease and heat stroke, reducing resilience over time. (5/10)

4/ 🏞️ Land-use changes exacerbate increasing heat exposure from global warming. Deforestation can remove natural cooling (shade, ET), urbanization worsens heat islands, and irrigation can increase humid heat stress. (4/10)

3/ 🌍 The toll isn't just physical—it's economic. Heat stress costs outdoor industries hundreds of billions of $USD annually if we estimate lost potential productivity. This burden falls hardest on those in agriculture, construction, and informal sectors, especially in low-income nations. (3/10)

2/ 💼 Today, about 20% of annual work hours in the tropics already exceed safe thresholds (ISO) for continuous heavy labor (acclimatized workers, ~415W intensity). At ~2°C global warming, this rises to 28%. By mid-century, nearly 800M workers may lose half their safe working hours annually. (2/10)

📚 For more details, read our paper: DOI: 10.1029/2024JD041150. Let’s discuss the implications for your region or field! 🌎 #ClimateResearch #PaleoScience
7/

🚩 Takeaway: Understanding the range of "natural" variability is critical for predicting how climate patterns will interact with warming—and for better water management in vulnerable regions. 💧 #Hydroclimate
6/

🌡️ Despite these challenges, DA reconstructions reveal changes in relationships between oceans and large-scale hydroclimate patterns that models alone often miss, offering crucial insights into natural variability before the industrial era.
5/

🧩 Caveats: We need more data, especially in the oceans! Proxy data availability (particularly in the early record) and model assumptions can introduce uncertainties. For example, reconstruction methods assume stable relationships between climate modes and proxies, which may oversimplify reality.
4/

🌊 Using DA (combining climate models + past paleoclimate data, like information from tree rings & corals), we reconstructed rainfall-SST relationships. We found shifts on decadal to centennial timescales in the southwestern U.S. and Lower Mississippi River Basin. 📜
3/

The key finding: teleconnections—links between patterns of different surface ocean temperatures and regional rainfall—are nonstationary. This means their strength and predictability vary over time due to natural climate variability. #ENSO #PDO #AMO
2/

🌟 This study shows another way decarbonization pays off. Reduced air pollution boosts solar panel efficiency, supporting the transition to renewables.
Want to dive deeper? 📖 Read the full paper here: doi.org/10.1021/acse...
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Some caveats:
1️⃣ Dust predictions vary most across models.
2️⃣ Rainfall's cleaning efficiency isn’t perfectly understood.
3️⃣ Factors like cloud cover weren’t included due to high uncertainty.
But the trends are clear. 🌞
(7/)

We combined:
1️⃣ #CMIP6 data on dust/pollutants under SSPs.
2️⃣ A solar transmittance model predicting losses.
3️⃣ Cleaning scenarios (manual vs. rainfall).
Focus: Human-made pollution vs. natural dust impacts.
(6/)