Hylke de Vries

Hawkins and Sutton (2009) developed a framework to partition total uncertainty in future climate projections into three components: internal variability, model uncertainty and scenario uncertainty. Here, we propose an extension of this framework that separates the contributions of global and regional processes. This enables a more physically based interpretation and improved understanding of the origins of projection uncertainty.

Photovoltaic (PV) energy production is crucial to achieve climate neutrality by 2050 and is tightly linked to climate variability and change. In particular, variations in the availability of surface solar radiation impact PV energy potential, while temperature and wind affect PV cell efficiency. Here, for the first time, we evaluate the ability of a 16-member km-scale convection-permitting climate model (CPMs) ensemble to reproduce PV energy potential over Italy. The assessment is conducted at station and national level using ERA5-land reanalysis dataset as reference. Driving climate variables are further evaluated against available high-resolution observations. The quality assessment of CPMs and ERA5-land data reveal an overestimation of downward shortwave radiation at the surface by CPMs (+6% in summer) and underestimation by similar amounts in ERA5-land compared to the high-resolution observational dataset. The 2m temperatures and 10 m wind speeds are slightly overestimated in CPMs as well by 1.24oC and 0.54 m/s respectively. Based on this good agreement, the energy potential model previously validated with ERA5-land is applied to CPMs. The CPM ensemble shows realistic diurnal and seasonal cycles but tends to overestimate PV potential relative to ERA5-land by up to +10 % at individual stations and +13 % at bidding zones, in particular due to the bias in radiation. Ensemble spread is 20% in summer, but significantly lower within model families. These findings support the use of CPM ensembles for climate change impact studies on solar resources, providing high resolution information to support adaptation strategies in power system planning at local scale.

Hawkins and Sutton (2009) developed a framework to partition total uncertainty in future climate projections into three components: internal variability, model uncertainty and scenario uncertainty. Here, we propose an extension of this framework that separates the contributions of global and regional processes. This enables a more physically based interpretation and improved understanding of the origins of projection uncertainty.

Extreme weather events have a disproportionate impact on society and are among the most tangible manifestations of anthropogenic climate change. Their inclusion in National climate services is therefore essential for informing climate risk assessments and adaptation planning. Framing future projections through storyline-based approaches anchored in well-remembered historical events offers a powerful means of connecting climate statistics to societal experience, thereby potentially improving understanding and usability.Here, we revisit the exceptional summer of 1976, now 50 years ago, which affected large parts of north-western Europe, including the Netherlands, Belgium, and the United Kingdom. 1976 remains one of the most severe drought and heat events in the instrumental record. The event was preconditioned by dry conditions in 1975 and the preceding winter, which depleted soil moisture and groundwater reserves, followed by persistent heatwave conditions during summer 1976 that further intensified drought through enhanced evapotranspiration.Using Pseudo Global Warming (PGW) experiments with a regional climate model, we place the 1976 event in present-day and future climate contexts. By conditioning on the observed large-scale circulation patterns, we quantify how the intensity and duration of drought and heat would change in progressively warmer climates. This approach allows a direct comparison between historically experienced extremes and plausible future analogues, and facilitates linkage with probabilistic regional climate projections.We aim to assess whether event-based frameworks for National Climate Scenarios and climate services can improve the clarity and effectiveness of communicating future climate risks, better inform the stress-testing of adaptation strategies, and further enhance stakeholder engagement across a range of decision-making contexts.

Precipitation projections over the Guiana region in northern South America are hampered by dry biases over that region in CMIP6 models. These biases are partly due to the wrong position of the inter tropical convergence zone (ITCZ) that is generally located too far to the south and unresolved mesoscale processes that are important for convective precipitation prevailing in that region. To account for that pseudo global warming (PGW) experiments with a regional climate model have been performed. In these experiments a perturbation derived from global climate models is imposed on present day simulations that are forced by reanalyses boundary conditions. The higher spatial resolution of regional climate models allows for a better simulation of mesoscale processes and using reanalyses as boundary conditions for the present climate induces an improved position of the ITCZ. For the present climate, the RACMO regional climate model with a resolution of about 12 KM, driven by ERA5 reanalyses boundary conditions, reveals a strong reduction of the dry bias. For the future climate the PGW experiments, perturbed by an ensemble mean of CMIP6 models, indicate a strong increase in precipitation, in particular during the wet season. This contrasts with CMIP6 models that indicate a drying for that region. This has strong implications for climate adaptation measures as presently this region is already affected by large floods induced by heavy rainfall. Present research is focused on changes in extreme precipitation and on different processes explaining these differences, such the position of the ITCZ, the role of orography and the simulation of mesoscale processes.

The Atlantic Meridional Overturning Circulation (AMOC) is an important feature of the Atlantic Ocean circulation transporting a large amount of heat into the North Atlantic (about 1.2 Pw at 26.5˚N), that strongly affects the Atlantic climate and its adjacent regions, including Western European and the Caribbean.One of the possible tipping points induced by global warming is a collapse of the AMOC. Recent research suggests that an irreversible AMOC collapse might already start this century even under low emission scenarios1,2,3. Using state-of-the-art climate models of the latest IPCC report (CMIP6) we have investigated the impact of such an imminent AMOC collapse for the Caribbean climate for a low emission scenario. On average the time scale for a total collapse is more than a century. In the worst case resulting in a severe reduction in annual mean rainfall for specific regions in the Caribbean of about 70%. Other significant implications of an AMOC collapse are a reduction of the increase in surface temperatures in the order of one degree °C, an increase in sea level rise of about 10 cm, and less favourable conditions for hurricane activity. These findings quantify Caribbean climate change impacts in the event of an AMOC collapse, complementing existing climate scenarios. ReferencesDitlevsen, P., & Ditlevsen, S. (2023). Warning of a forthcoming collapse of the Atlantic meridional overturning circulation. Nature Communications, 14(1), 1-12.Drijfhout, S., Angevaare, J. R., Mecking, J., van Westen, R. M., & Rahmstorf, S. (2025). Shutdown of northern Atlantic overturning after 2100 following deep mixing collapse in CMIP6 projections. Environmental Research Letters, 20(9), 094062.Rahmstorf, S. (2024). Is the Atlantic overturning circulation approaching a tipping point? Oceanography, 37(3), 16-29.

Hawkins and Sutton (2009) developed a framework to partition total uncertainty in future climate projections into three components: internal variability, model uncertainty and scenario uncertainty. Here, we propose an extension of this framework that separates the contributions of global and regional processes. This enables a more physically based interpretation and improved understanding of the origins of projection uncertainty.

Hawkins and Sutton (2009) developed a framework to partition total uncertainty in future climate projections into three components: internal variability, model uncertainty and scenario uncertainty. Here, we propose an extension of this framework that separates the contributions of global and regional processes. This enables a more physically based interpretation and improved understanding of the origins of projection uncertainty.