ALBERT

Description: beliebiger Attribution in its general definition aims to quantify drivers of change in a system. According to IPCC Working Group II (WGII) a change in a natural, human or managed system is attributed to climate change by quantifying the difference between the observed state of the system and a counterfactual baseline that characterizes the system's behavior in the absence of climate change, where “climate change refers to any long-term trend in climate, irrespective of its cause” (IPCC, 2014). Impact attribution following this definition remains a challenge because the counterfactual baseline, which characterizes the system behavior in the hypothetical absence of climate change, cannot be observed. Process-based and empirical impact models can fill this gap as they allow us to simulate the counterfactual climate impact baseline. In those simulations, the models are forced by observed direct (human) drivers such as land use changes, changes in water or agricultural management but a counterfactual climate without long-term changes. We here present ATTRICI (ATTRIbuting Climate Impacts), an approach to construct the required counterfactual stationary climate data from observational (factual) climate data. Our method identifies the long-term shifts in the considered daily climate variables that are correlated to global mean temperature change assuming a smooth annual cycle of the associated scaling coefficients for each day of the year. The produced counterfactual climate datasets are used as forcing data within the impact attribution setup of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a). Our method preserves the internal variability of the observed data in the sense that factual and counterfactual data for a given day have the same rank in their respective statistical distributions. The associated impact model simulations allow for quantifying the contribution of climate change to observed long-term changes in impact indicators and for quantifying the contribution of the observed trend in climate to the magnitude of individual impact events. Attribution of climate impacts to anthropogenic forcing would need an additional step separating anthropogenic climate forcing from other sources of climate trends, which is not covered by our method.

Description: This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Description: Rising seas are a threat to human and natural systems along coastlines. The relation between global warming and sea level rise is established, but the quantification of impacts of historical sea level rise on a global scale is largely absent. To foster such quantification, here we present a reconstruction of historical hourly (1979–2015) and monthly (1900–2015) coastal water levels and a corresponding counterfactual without long-term trends in sea level. The dataset pair allows for impact attribution studies that quantify the contribution of sea level rise to observed changes in coastal systems following the definition of the Intergovernmental Panel on Climate Change (IPCC). Impacts are ultimately caused by water levels that are relative to the local land height, which makes the inclusion of vertical land motion a necessary step. Also, many impacts are driven by sub-daily extreme water levels. To capture these aspects, the factual data combine reconstructed geocentric sea level on a monthly timescale since 1900, vertical land motion since 1900 and hourly storm-tide variations since 1979. The inclusion of observation-based vertical land motion brings the trends of the combined dataset closer to tide gauge records in most cases, but outliers remain. Daily maximum water levels get in closer agreement with tide gauges through the inclusion of intra-annual ocean density variations. The counterfactual data are derived from the factual data through subtraction of the quadratic trend. The dataset is made available openly through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) at https://doi.org/10.48364/ISIMIP.749905 (Treu et al., 2023a).

Description: Data to reproduce the analysis of the Hourly Coastal water levels with Counterfactual (HCC) dataset, presented in the publication "Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution" published in Earth System Science Data (ESSD).

Description: This Zenodo archive contains essential input datasets utilized in our research study titled "Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution". This archive contains only input data. The Hourly Coastal water levels with Counterfactual (HCC) dataset is published in the ISIMIP repository.

Description: Source code to produce the data, analysis and figures for the paper "Reconstruction of hourly coastal water levels and counterfactuals without sea level rise for impact attribution" which is published in Earth System Science Data (ESSD)

Description: Rising seas are a threat for human and natural systems along coastlines. The relation between global warming and sea-level rise is established, but impacts of historical sea-level rise are not well quantified on a global scale. To foster the attribution of observed coastal impacts to sea-level rise, we present a reconstruction of historical hourly (1979-2015) and monthly (1900-2015) coastal water levels. Alongside this ‘factual’ data we provide associated ‘counterfactual’ data where long term trends in sea level have been removed. The pair of data sets has been generated to allow for impact studies attributing observed changes in coastal systems to observed sea level rise. The factual data combines long-term changes of geocentric sea-level rise and vertical land motion with hourly storm-tide variations. Comparison to tide gauge records shows improved performance of the combined data set compared to its individual components on sub-yearly timescales, mainly due to the inclusion of density-driven sea-level change. On annual to decadal timescales, inclusion of observation-based vertical land motion brings the combined data set close to tide gauge records in most cases, but outliers remain. The dataset is made available openly through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).