Global warming likely affects regional droughts across Eurasia. The finding has implications for understanding climate change and the need to adapt to new drought-related risks.

Researchers used long-term climate reconstruction records based on tree-ring data to see how global warming affects the risk of droughtThese records provide data on climate variability before the Industrial Revolution, allowing comparison with current conditions.

The findings suggest that Current droughts in some areas are not well explained by natural climate variability and are changing in an unprecedented way due to rising global temperatures.

Key points of the study:

• Climate change can be detected and its causes attributed even without the use of general circulation model (GCM) simulations.
• Hydroclimatic patterns in Eurasia in the 21st century are inconsistent with the variability of the pre-industrial period, which was estimated from tree-ring-based records.
• Global warming has likely affected both wet and dry conditions across Eurasia.

To evaluate the impact of global warming, scientists used Palmer Drought Severity Index (PDSI), which is commonly used to measure drought risk. They analyzed PDSI records from the large-scale Eurasian Drought Atlas (GEDA), which contains tree-ring data dating back to 1000 AD. Using a Bayesian framework, they estimated the temporal and spatial characteristics of hydroclimate variability before 1850.

Results studies indicate that Global warming has contributed to drier PDSI conditions in Eastern Europe, the Mediterranean and Arctic Russia, and to wetter PDSI conditions in Northern Europe, East and Central Asia and Tibet.These findings are consistent with predictions made in the IPCC's Sixth Assessment Report, increasing confidence in climate models.

To determine the extent to which global warming has affected regional changes in PDSI, the researchers compared two models: a natural variability model (MΣ2) and a temperature-dependent forced response (MFT) model. They found that The MFT model, which takes into account the influence of global average temperature, better explains recent PDSI changes across Eurasia than the MΣ2 model.

The study also highlights the importance of taking into account the complexity of natural climate variability. It found that year-to-year soil moisture persistence and interconnections between different regions influence wetness or drought trendsNevertheless, the findings suggest that rising global temperatures are becoming an increasingly important factor in determining drought risk.

The researchers emphasize the need to understand the underlying physical factors that shape the relationship between global warming and regional drought. They also emphasize the need for measures to adapt to the changed risk of drought in a warming world.

In conclusion, this study provides evidence that Global warming is affecting regional drought patterns in EurasiaUsing extensive tree-ring data and sophisticated statistical methods, the research offers new insights into the impact of climate change on drought and highlights the need for informed adaptation strategies. Spring

The article is published   at AGU Advances

Kate Marvel, Benjamin I. Cook, Edward Cook

Glossary of key terms

• Palmer Drought Severity Index (PDSI): A commonly used drought risk indicator that reflects soil moisture based on temperature and precipitation.
• Great Eurasian Drought Atlas (GEDA): Reconstruction of past hydroclimate variability based on tree rings, covering the last millennium.
• General Circulation Models (GCMs): Numerical models representing physical processes in the atmosphere, ocean and land surface.
• Bayesian Framework: A statistical method that updates probabilities for hypotheses when additional evidence becomes available.
• Pre-industrial Variability: Natural climate fluctuations before significant human influence, usually defined as the period before 1850.
• Forced Response: Climate change that is directly caused by an external factor, such as global warming.
• Spatial Fingerprint: A characteristic spatial pattern of climate change that is associated with a specific forcing factor.
• Leave-One-Out Cross-Validation (LOO-CV): A model evaluation technique where one data point is left aside and the model is trained on the remaining data.
• Posterior Predictive Distribution (PPD): Distribution of predicted values based on posterior distributions of model parameters.
• Teleconnections: The relationship between climatic events that occur at great distances from each other.