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Het KNMI presenteert vier nieuwe scenario’s voor klimaatverandering in Nederland inclusief Caribisch Nederland rond 2050 en 2100. De KNMI’23-klimaatscenario’s zijn gebaseerd op de laatste inzichten en vervangen de klimaatscenario’s uit 2014. Voor zeespiegelstijging is gebruik gemaakt van de nieuwe inzichten van het IPCC over het mogelijk versneld afkalven van de Antarctische IJskap. In KNMI’23 is de droogteproblematiek in Nederland en omgeving beter in kaart gebracht en zijn de veranderingen in neerslagextremen in zomerbuien beter onderbouwd. Verder zijn de schattingen van de wereldwijde temperatuurstijging voor een bepaalde toename van broeikasgassen nauwkeuriger. Net als na 2014 kunnen de nieuwe klimaatscenario’s worden gebruikt om de effecten op verschillende sectoren door te rekenen. Hiermee worden de risicoanalyses uit de Nationale Adaptatie Strategie geactualiseerd. Ook worden de Deltascenario’s van het Deltaprogramma gebaseerd op de combinatie van de KNMI’23-klimaatscenario’s en de sociaaleconomische scenario’s voor Nederland van het PBL (de WLO-scenario’s). De klimaatscenario’s voor Caribisch Nederland dienen als uitgangspunt voor adaptatiebeleid voor Bonaire, Sint Eustatius en Saba (BES-eilanden).

The assessment of return periods of extreme hydrological events often relies on statistical analysis using generalized extreme value (GEV) distributions. Here we compare the traditional GEV approach with a novel large ensemble approach to determine the added value of a direct, empirical distribution-based estimate of extreme hydrological events. Using the global climate and hydrological models EC-Earth and PCR-GLOBWB, we simulate 2,000 years of global hydrology for a present-day and 2 °C warmer climate. We show that the GEV method has inherent limitations in estimating changes in hydrological extremes, especially for compound hydrological events. The large ensemble method does not suffer from these limitations and quantifies the impacts of climate change with greater precision. The explicit simulation of extreme events enables better hydrological process understanding. We conclude that future studies focusing on the impact of climatic changes on hydrological extremes should use large ensemble techniques to properly account for these rare hydrological events.

Conditional Markov chain (CMC) models have proven to be promising building blocks for stochastic convection parameterizations. In this paper, it is demonstrated how two different CMC models can be used as mass flux closures in convection parameterizations. More specifically, the CMC models provide a stochastic estimate of the convective area fraction that is directly proportional to the cloud-base mass flux. Since, in one of the models, the number of CMCs decreases with increasing resolution, this approach makes convection parameterizations scale aware and introduces stochastic fluctuations that increase with resolution in a realistic way. Both CMC models are implemented in a GCM of intermediate complexity. It is shown that with the CMC models, trained with observational data, it is possible to improve both the subgrid-scale variability and the autocorrelation function of the cloud-base mass flux as well as the distribution of the daily accumulated precipitation in the tropics. Hovmöller diagrams and wavenumber-frequency diagrams of the equatorial precipitation indicate that, in this specific GCM, convectively coupled equatorial waves are more sensitive to the mean cloud-base mass flux than to stochastic fluctuations. A smaller mean mass flux tends to increase the power of the simulated MJO and to diminish equatorial Kelvin waves.

Conditional Markov chain (CMC) models have proven to be promising building blocks for stochastic convection parameterizations. In this paper, it is demonstrated how two different CMC models can be used as mass flux closures in convection parameterizations. More specifically, the CMC models provide a stochastic estimate of the convective area fraction that is directly proportional to the cloud-base mass flux. Since, in one of the models, the number of CMCs decreases with increasing resolution, this approach makes convection parameterizations scale aware and introduces stochastic fluctuations that increase with resolution in a realistic way. Both CMC models are implemented in a GCM of intermediate complexity. It is shown that with the CMC models, trained with observational data, it is possible to improve both the subgrid-scale variability and the autocorrelation function of the cloud-base mass flux as well as the distribution of the daily accumulated precipitation in the tropics. Hovmöller diagrams and wavenumber–frequency diagrams of the equatorial precipitation indicate that, in this specific GCM, convectively coupled equatorial waves are more sensitive to the mean cloud-base mass flux than to stochastic fluctuations. A smaller mean mass flux tends to increase the power of the simulated MJO and to diminish equatorial Kelvin waves., Geoscience and Remote Sensing, Civil Engineering and Geosciences

To test the impact of modeling uncertainties and biases on the simulation of cloud feedbacks, several configurations of the EC-Earth climate model are built altering physical parameterizations. An overview of the various radiative feedbacks diagnosed from the reference EC-Earth configuration is documented for the first time. The cloud feedback is positive and small. While the total feedback parameter is almost insensitive to model configuration, the cloud feedback, in particular its shortwave (SW) component, can vary considerably depending on the model settings. The lateral mass exchange rate of penetrative convection and the conversion rate from condensed water to precipitation are leading uncertain parameters affecting the radiative feedbacks diagnosed. Consistent with other studies, we find a strong correlation between low-cloud model fidelity and low-cloud response under global warming. It is shown that this relationship holds only for stratocumulus regimes and is contributed by low-cloud cover, rather than low-cloud optical thickness. Model configurations simulating higher stratocumulus cover, which is closer to the observations, exhibit a stronger positive SW cloud feedback. This feedback is likely underestimated in the reference EC-Earth configuration, over the eastern basins of the tropical oceans. In addition, connections between simulated high-cloud top altitude in present-day climate and longwave cloud feedback are discussed., Geoscience & Remote Sensing, Civil Engineering and Geosciences

With the uprising climate problems like the warming due to greenhouse gasses, the need for future climate scenarios is growing fast. Many state of the art climate models exist, but they are imperfect and give a large variaty of future scenarios. In this thesis we try to find a way to improve the predictions of climate models by combining them. Multiple imperfect models will exchange information during the simulations and are combined into a super-model. The idea is not only that the imperfect models can use each others strengths, but also that synchronization between the models can occur, such that a mutual prediction results. The objective is to find a way to let models exchange information such that the resulting super-model gives a better prediction than any of the separate imperfect models. A learning process is developed to objectively determine the exchange of information between the models. The approach is tested on small chaotic dynamical systems that have similar properties as the atmosphere. The system with standard parameter values is regarded as truth and three imperfect models are created by perturbing these parameter values. By using the small chaotic dynamical systems we can use the information of the truth to see how well the approach works.

The main characteristics of the new version 1.2 of the three-dimensional Earth system model of intermediate complexity LOVECLIM are briefly described. LOVECLIM 1.2 includes representations of the atmosphere, the ocean and sea ice, the land surface (including vegetation), the ice sheets, the icebergs and the carbon cycle. The atmospheric component is ECBilt2, a T21, 3-level quasi-geostrophic model. The ocean component is CLIO3, which consists of an ocean general circulation model coupled to a comprehensive thermodynamic-dynamic sea-ice model. Its horizontal resolution is of 3° by 3°, and there are 20 levels in the ocean. ECBilt-CLIO is coupled to VECODE, a vegetation model that simulates the dynamics of two main terrestrial plant functional types, trees and grasses, as well as desert. VECODE also simulates the evolution of the carbon cycle over land while the ocean carbon cycle is represented by LOCH, a comprehensive model that takes into account both the solubility and biological pumps. The ice sheet component AGISM is made up of a three-dimensional thermomechanical model of the ice sheet flow, a visco-elastic bedrock model and a model of the mass balance at the ice-atmosphere and ice-ocean interfaces. For both the Greenland and Antarctic ice sheets, calculations are made on a 10 km by 10 km resolution grid with 31 sigma levels. LOVECLIM1.2 reproduces well the major characteristics of the observed climate both for present-day conditions and for key past periods such as the last millennium, the mid-Holocene and the Last Glacial Maximum. However, despite some improvements compared to earlier versions, some biases are still present in the model. The most serious ones are mainly located at low latitudes with an overestimation of the temperature there, a too symmetric distribution of precipitation between the two hemispheres, and an overestimation of precipitation and vegetation cover in the subtropics. In addition, the atmospheric circulation is too weak. The model also

The main characteristics of the new version 1.2 of the three-dimensional Earth system model of intermediate complexity LOVECLIM are briefly described. LOVECLIM 1.2 includes representations of the atmosphere, the ocean and sea ice, the land surface (including vegetation), the ice sheets, the icebergs and the carbon cycle. The atmospheric component is ECBilt2, a T21, 3-level quasi-geostrophic model. The ocean component is CLIO3, which consists of an ocean general circulation model coupled to a comprehensive thermodynamic-dynamic sea-ice model. Its horizontal resolution is of 3° by 3°, and there are 20 levels in the ocean. ECBilt-CLIO is coupled to VECODE, a vegetation model that simulates the dynamics of two main terrestrial plant functional types, trees and grasses, as well as desert. VECODE also simulates the evolution of the carbon cycle over land while the ocean carbon cycle is represented by LOCH, a comprehensive model that takes into account both the solubility and biological pumps. The ice sheet component AGISM is made up of a three-dimensional thermomechanical model of the ice sheet flow, a visco-elastic bedrock model and a model of the mass balance at the ice-atmosphere and ice-ocean interfaces. For both the Greenland and Antarctic ice sheets, calculations are made on a 10 km by 10 km resolution grid with 31 sigma levels. LOVECLIM1.2 reproduces well the major characteristics of the observed climate both for present-day conditions and for key past periods such as the last millennium, the mid-Holocene and the Last Glacial Maximum. However, despite some improvements compared to earlier versions, some biases are still present in the model. The most serious ones are mainly located at low latitudes with an overestimation of the temperature there, a too symmetric distribution of precipitation between the two hemispheres, and an overestimation of precipitation and vegetation cover in the subtropics. In addition, the atmospheric circulation is too weak. The model also

The main characteristics of the new version 1.2 of the three-dimensional Earth system model of intermediate complexity LOVECLIM are briefly described. LOVECLIM 1.2 includes representations of the atmosphere, the ocean and sea ice, the land surface (including vegetation), the ice sheets, the icebergs and the carbon cycle. The atmospheric component is ECBilt2, a T21, 3-level quasi-geostrophic model. The ocean component is CLIO3, which consists of an ocean general circulation model coupled to a comprehensive thermodynamic-dynamic sea-ice model. Its horizontal resolution is of 3° by 3°, and there are 20 levels in the ocean. ECBilt-CLIO is coupled to VECODE, a vegetation model that simulates the dynamics of two main terrestrial plant functional types, trees and grasses, as well as desert. VECODE also simulates the evolution of the carbon cycle over land while the ocean carbon cycle is represented by LOCH, a comprehensive model that takes into account both the solubility and biological pumps. The ice sheet component AGISM is made up of a three-dimensional thermomechanical model of the ice sheet flow, a visco-elastic bedrock model and a model of the mass balance at the ice-atmosphere and ice-ocean interfaces. For both the Greenland and Antarctic ice sheets, calculations are made on a 10 km by 10 km resolution grid with 31 sigma levels. LOVECLIM1.2 reproduces well the major characteristics of the observed climate both for present-day conditions and for key past periods such as the last millennium, the mid-Holocene and the Last Glacial Maximum. However, despite some improvements compared to earlier versions, some biases are still present in the model. The most serious ones are mainly located at low latitudes with an overestimation of the temperature there, a too symmetric distribution of precipitation between the two hemispheres, and an overestimation of precipitation and vegetation cover in the subtropics. In addition, the atmospheric circulation is too weak. The, info:eu-repo/semantics/published

The mean state and variability of deep convection in the ocean influence the North Atlantic climate. Using an ensemble experiment with a coupled atmosphere–ocean–sea ice model, it is shown that cooling and subdued warming areas can occur over the North Atlantic Ocean and adjacent landmasses under global warming. Different ‘‘present-day’’ convection patterns in the Greenland–Iceland–Norway (GIN) Sea result in different future surface- air temperature changes. At higher latitudes, the more effective positive sea ice feedback increases the likelihood of changes in convection causing a regional cooling that is larger than the warming brought about by the enhanced greenhouse effect. The modeled freshening of deep ocean layers in the North Atlantic in a time period preceding a reorganization of GIN Sea convection is consistent with recent observations. Low-frequency internal variability in the ocean model has relatively little impact on the response patterns.