Periodic Reporting for period 1 - DeCaGloPreCEs (Decadal to multi-deCadal Global Predictions of Compound Events)
Période du rapport: 2022-11-01 au 2024-10-31
Climate extremes, such as heatwaves, droughts, and their compounded combinations, pose significant threats to socio-economic systems globally. These events disrupt agriculture, water resources, public health, and infrastructure, often leading to catastrophic consequences for communities and economies. Under the influence of anthropogenic climate change, such extreme events are projected to become not only more frequent but also more intense. This escalation underscores the urgent need to improve our ability to predict these occurrences through skillful and reliable climate predictions. By enhancing our understanding and forecasting capabilities, we can better prepare for and mitigate the impacts of these extremes.
This project is designed with the primary aim of addressing critical gaps in our predictive understanding of hot, dry, and compound hot-dry extremes. Specifically, it seeks to assess the prediction skill of these events by analyzing the role of natural climate variability on regional and global scales. Another central objective of the project is to provide robust future projections of how heatwaves, droughts, and their combined occurrences are likely to evolve under various climate change scenarios. Using state-of-the-art climate models and observational datasets, the project will quantify expected changes in the frequency, intensity, and spatial extent of these events. This information is vital for policymakers and stakeholders, enabling them to make informed decisions about adaptation and mitigation strategies.
In addition to these predictive and projection-focused goals, the project aims to delve deeper into the physical mechanisms that underpin the sources of predictability for these events. By identifying and quantifying the key drivers—such as teleconnections—the project will enhance our understanding of the complex systems that govern regional hot and dry extremes. Understanding these processes is crucial for refining climate models and improving their predictive capabilities. In conclusion, this project is a comprehensive effort to enhance the predictability, understanding, and management of hot, dry, and compound hot-dry extremes. By focusing on natural climate variability, future projections, and physical mechanisms, it seeks to provide a strong foundation for mitigating the growing risks associated with these climate extremes in a warming world.
This project is designed with the primary aim of addressing critical gaps in our predictive understanding of hot, dry, and compound hot-dry extremes. Specifically, it seeks to assess the prediction skill of these events by analyzing the role of natural climate variability on regional and global scales. Another central objective of the project is to provide robust future projections of how heatwaves, droughts, and their combined occurrences are likely to evolve under various climate change scenarios. Using state-of-the-art climate models and observational datasets, the project will quantify expected changes in the frequency, intensity, and spatial extent of these events. This information is vital for policymakers and stakeholders, enabling them to make informed decisions about adaptation and mitigation strategies.
In addition to these predictive and projection-focused goals, the project aims to delve deeper into the physical mechanisms that underpin the sources of predictability for these events. By identifying and quantifying the key drivers—such as teleconnections—the project will enhance our understanding of the complex systems that govern regional hot and dry extremes. Understanding these processes is crucial for refining climate models and improving their predictive capabilities. In conclusion, this project is a comprehensive effort to enhance the predictability, understanding, and management of hot, dry, and compound hot-dry extremes. By focusing on natural climate variability, future projections, and physical mechanisms, it seeks to provide a strong foundation for mitigating the growing risks associated with these climate extremes in a warming world.
Multi-decadal prediction skill of univariate hot, cold and dry extremes (one paper published, De Luca et al. 2023 https://iopscience.iop.org/article/10.1088/1748-9326/acf389/meta)(s’ouvre dans une nouvelle fenêtre):
We computed the multi-decadal prediction skill of hot and dry extremes over global land regions by constraining decadal variability in a large CMIP6 multi-model ensemble within the 1961-2019 period. In the analysis we showed how good our constrained ensemble is compared to observations and the unconstrained ensemble, along with its added value. We also showed the prediction skill for the 20-year average time-series over selected regions around the world. Hot extremes were computed with Climpact indices, whereas dry extremes were computed with SPI and SPEI monthly indices (< -1).
Multi-annual prediction skill of hot, dry and compound hot-dry extremes (one paper under preparation):
We performed an analysis of multi-annual prediction skill for univariate and compound hot-dry extremes at the global scale, over land regions. We used state-of-the-art climate prediction systems (i.e. DCPP) and observations. We then assessed the multi-annual skill of the extremes against the observations for the 1961-2014 period. Then we also quantified the added value of the DCPP multi-annual predictions compared to the observations and showed the regions where the skill is positive and then quantified the contribution of the univariate extremes in driving the compound ones. For temperature extremes we used temperature data exceeding the 90th percentile, whereas for dry extremes we used the annual count of dry months computed with SPI and SPEI monthly indices (< -1). Compound extremes have been computed by counting the number of temperature and dry extremes within each year.
Future projections of global land-only hot, dry and compound hot-dry extremes (one paper published, De Luca & Donat 2023 https://doi.org/10.1029/2022GL102493)(s’ouvre dans une nouvelle fenêtre):
Here we used a multi-model ensemble of 25 CMIP6 models, the historical (1950-2014) and four different SSP emission and adaptation scenarios (2015-2100), namely SSP1-2.6 SSP2-4.5 SSP3-7.0 and SSP5-8.5. For all the models and scenarios I computed five hot extreme Climpact indices, two indices for dry extremes from SPI and SPEI (annual count of dry months) and two indices for compound hot-dry extremes (annual count of same-day hot and dry extremes). With these data, I computed end-of-century difference maps (2081-2100 vs 1981-2000) and global land-only everage time-series from 1950 to 2100.
Decadal prediction skill and sources of predictability of Australian drought (one paper under preparation):
We investigated decadal predictions of austral summer drought in south-eastern Australia within the 1961-2019 period, along with its sources of predictability. We constrained decadal variability from a large CMIP6 multi-model ensemble and after quantifying the decadal skill against observations such as GPCC and BEST I quantified the sources of this predictability by looking at sea-level pressure, sea-surface temperature patterns and modes of climate variability such as the Southern Annular Mode. Decadal variability was constrained using different SST regions, such as the global Ocean, north Atlantic, Atlantic, Pacific, Indian and Southern Oceans.
Blocking frequencies in very-high resolution climate model simulations (one paper published, De Luca et al. 2024 https://doi.org/10.1029/2024GL111016)(s’ouvre dans une nouvelle fenêtre):
We analysed the blocking frequency in very-high resolution idealised climate model simulations and compared it to lower resolution ones. This work addresses an important research gap since blocking frequencies are underestimated by current climate models. Blocking is a dynamical mechanism that, during summer, drives hot, dry and compound hot-dry extremes.
We computed the multi-decadal prediction skill of hot and dry extremes over global land regions by constraining decadal variability in a large CMIP6 multi-model ensemble within the 1961-2019 period. In the analysis we showed how good our constrained ensemble is compared to observations and the unconstrained ensemble, along with its added value. We also showed the prediction skill for the 20-year average time-series over selected regions around the world. Hot extremes were computed with Climpact indices, whereas dry extremes were computed with SPI and SPEI monthly indices (< -1).
Multi-annual prediction skill of hot, dry and compound hot-dry extremes (one paper under preparation):
We performed an analysis of multi-annual prediction skill for univariate and compound hot-dry extremes at the global scale, over land regions. We used state-of-the-art climate prediction systems (i.e. DCPP) and observations. We then assessed the multi-annual skill of the extremes against the observations for the 1961-2014 period. Then we also quantified the added value of the DCPP multi-annual predictions compared to the observations and showed the regions where the skill is positive and then quantified the contribution of the univariate extremes in driving the compound ones. For temperature extremes we used temperature data exceeding the 90th percentile, whereas for dry extremes we used the annual count of dry months computed with SPI and SPEI monthly indices (< -1). Compound extremes have been computed by counting the number of temperature and dry extremes within each year.
Future projections of global land-only hot, dry and compound hot-dry extremes (one paper published, De Luca & Donat 2023 https://doi.org/10.1029/2022GL102493)(s’ouvre dans une nouvelle fenêtre):
Here we used a multi-model ensemble of 25 CMIP6 models, the historical (1950-2014) and four different SSP emission and adaptation scenarios (2015-2100), namely SSP1-2.6 SSP2-4.5 SSP3-7.0 and SSP5-8.5. For all the models and scenarios I computed five hot extreme Climpact indices, two indices for dry extremes from SPI and SPEI (annual count of dry months) and two indices for compound hot-dry extremes (annual count of same-day hot and dry extremes). With these data, I computed end-of-century difference maps (2081-2100 vs 1981-2000) and global land-only everage time-series from 1950 to 2100.
Decadal prediction skill and sources of predictability of Australian drought (one paper under preparation):
We investigated decadal predictions of austral summer drought in south-eastern Australia within the 1961-2019 period, along with its sources of predictability. We constrained decadal variability from a large CMIP6 multi-model ensemble and after quantifying the decadal skill against observations such as GPCC and BEST I quantified the sources of this predictability by looking at sea-level pressure, sea-surface temperature patterns and modes of climate variability such as the Southern Annular Mode. Decadal variability was constrained using different SST regions, such as the global Ocean, north Atlantic, Atlantic, Pacific, Indian and Southern Oceans.
Blocking frequencies in very-high resolution climate model simulations (one paper published, De Luca et al. 2024 https://doi.org/10.1029/2024GL111016)(s’ouvre dans une nouvelle fenêtre):
We analysed the blocking frequency in very-high resolution idealised climate model simulations and compared it to lower resolution ones. This work addresses an important research gap since blocking frequencies are underestimated by current climate models. Blocking is a dynamical mechanism that, during summer, drives hot, dry and compound hot-dry extremes.
Multi-decadal prediction skill of univariate hot, cold and dry extremes (one paper published, De Luca et al. 2023 https://iopscience.iop.org/article/10.1088/1748-9326/acf389/meta)(s’ouvre dans une nouvelle fenêtre):
Here we obtained positive skill and added value for hot and cold extremes over most of the globe but less positive skill for dry extremes, also depending on the index used to compute them. Here the three evaluation metrics, namely correlation, RMSSS and RPSS show positive skill for the Tx90p and TXx indices, with higher and more widespread skill found for Tx90p. Added value for the constrained ensemble is also found over large parts of the globe, when computed with residual correlation, RMSSS and RPSS against the full ensemble. Dry extremes showed less positive and significant skill compared to hot and cold extremes, and we also noticed that dry extremes computed with SPEI had more positive skill overall, given the contribution of the global warming trend within the index. We identified four key regions where hot, cold and dry extremes are skilful at multi-decadal scale and they are: north-western north America, south-eastern China, south-eastern Australia and south-eastern central Europe. Time-series of 20-year averages over the above regions also showed positive and significant skill for the constrained ensemble when compared to observations and the full ensemble.
Multi-annual prediction skill of hot, dry and compound hot-dry extremes (one paper under preparation):
So far, results showed positive skill over most of the globe for hot extremes, whereas skill is less positive for dry extremes, although we can clearly identify certain regions where it is high. For compound hot-dry extremes, skill is positive in large parts of the globe, especially for the extremes computed with the SPEI index. When looking at the DCPP added value in the multi-annual predictions we notice that there is positive skill in selected regions of the globe, for example in southern Australia for hot and dry extremes, the latter computed with SPEI; central Asia for hot extremes, the Mediterranean for compound extremes and south-western USA for all the extreme indices. Lastly, the correlations between compound extremes and their univariate counterparts show positive and significant signs, especially for the observations when compared to the DCPP. Correlations are especially higher for dry extremes computed with SPEI, indicating that they are the ones driving more the compound extremes.
Future projections of global land-only hot, dry and compound hot-dry extremes (one paper published, De Luca & Donat 2023 https://doi.org/10.1029/2022GL102493)(s’ouvre dans une nouvelle fenêtre):
Results showed a projected increase in hot, dry and compound hot-dry extremes over most of the globe under the highest-emissions/lowest adaptation scenario. However, results for dry extremes are sensitive to the index used to compute the hazard. For dry extremes computed with SPI (i.e. based on precipitation only) their frequency increases and decreases depending on the world’s region. On the other hand, dry extremes computed with SPEI (i.e. based on precipitation and evapotranspiration) are projected to increase by 2100 over most of the globe. A significant decrease in projected extremes is obtained with lower emission/higher adaptation scenarios and this points to the importance of deploying strategies of climate change mitigation and adaptation.
Decadal prediction skill and sources of predictability of Australian drought (one paper under preparation):
Results showed that by constraining decadal variability one can obtain similar prediction skill of summer drought than state-of-the-art prediction systems (i.e. DCPP). Then, we also showed that summer drought in the region is linked to the Southern Annual Mode (SAM), which we are also able to skilfully predict. This means that by predicting the SAM we are able to skilfully predict summer drought in south-eastern Australia.
Blocking frequencies in very-high resolution climate model simulations (one paper published, De Luca et al. 2024 https://doi.org/10.1029/2024GL111016)(s’ouvre dans une nouvelle fenêtre):
Here we showed that blocking frequencies, computed with the anomaly (ANM) and absolute (ABS) methods, are enhanced in very-high resolution climate model simulations, making it more comparable to observations. Moreover, we showed that the blocking events identified are linked to anti-cyclonic and cyclonic Rossby-wave breaking mechanisms. This is an important step suggesting that the next generation of climate models will be more accurate in simulating blocking events, which are responsible for hot, dry and compound hot-dry extremes over the mid-latitudes.
Here we obtained positive skill and added value for hot and cold extremes over most of the globe but less positive skill for dry extremes, also depending on the index used to compute them. Here the three evaluation metrics, namely correlation, RMSSS and RPSS show positive skill for the Tx90p and TXx indices, with higher and more widespread skill found for Tx90p. Added value for the constrained ensemble is also found over large parts of the globe, when computed with residual correlation, RMSSS and RPSS against the full ensemble. Dry extremes showed less positive and significant skill compared to hot and cold extremes, and we also noticed that dry extremes computed with SPEI had more positive skill overall, given the contribution of the global warming trend within the index. We identified four key regions where hot, cold and dry extremes are skilful at multi-decadal scale and they are: north-western north America, south-eastern China, south-eastern Australia and south-eastern central Europe. Time-series of 20-year averages over the above regions also showed positive and significant skill for the constrained ensemble when compared to observations and the full ensemble.
Multi-annual prediction skill of hot, dry and compound hot-dry extremes (one paper under preparation):
So far, results showed positive skill over most of the globe for hot extremes, whereas skill is less positive for dry extremes, although we can clearly identify certain regions where it is high. For compound hot-dry extremes, skill is positive in large parts of the globe, especially for the extremes computed with the SPEI index. When looking at the DCPP added value in the multi-annual predictions we notice that there is positive skill in selected regions of the globe, for example in southern Australia for hot and dry extremes, the latter computed with SPEI; central Asia for hot extremes, the Mediterranean for compound extremes and south-western USA for all the extreme indices. Lastly, the correlations between compound extremes and their univariate counterparts show positive and significant signs, especially for the observations when compared to the DCPP. Correlations are especially higher for dry extremes computed with SPEI, indicating that they are the ones driving more the compound extremes.
Future projections of global land-only hot, dry and compound hot-dry extremes (one paper published, De Luca & Donat 2023 https://doi.org/10.1029/2022GL102493)(s’ouvre dans une nouvelle fenêtre):
Results showed a projected increase in hot, dry and compound hot-dry extremes over most of the globe under the highest-emissions/lowest adaptation scenario. However, results for dry extremes are sensitive to the index used to compute the hazard. For dry extremes computed with SPI (i.e. based on precipitation only) their frequency increases and decreases depending on the world’s region. On the other hand, dry extremes computed with SPEI (i.e. based on precipitation and evapotranspiration) are projected to increase by 2100 over most of the globe. A significant decrease in projected extremes is obtained with lower emission/higher adaptation scenarios and this points to the importance of deploying strategies of climate change mitigation and adaptation.
Decadal prediction skill and sources of predictability of Australian drought (one paper under preparation):
Results showed that by constraining decadal variability one can obtain similar prediction skill of summer drought than state-of-the-art prediction systems (i.e. DCPP). Then, we also showed that summer drought in the region is linked to the Southern Annual Mode (SAM), which we are also able to skilfully predict. This means that by predicting the SAM we are able to skilfully predict summer drought in south-eastern Australia.
Blocking frequencies in very-high resolution climate model simulations (one paper published, De Luca et al. 2024 https://doi.org/10.1029/2024GL111016)(s’ouvre dans une nouvelle fenêtre):
Here we showed that blocking frequencies, computed with the anomaly (ANM) and absolute (ABS) methods, are enhanced in very-high resolution climate model simulations, making it more comparable to observations. Moreover, we showed that the blocking events identified are linked to anti-cyclonic and cyclonic Rossby-wave breaking mechanisms. This is an important step suggesting that the next generation of climate models will be more accurate in simulating blocking events, which are responsible for hot, dry and compound hot-dry extremes over the mid-latitudes.