2017, Piniewski, Mikołaj, Meresa, Hadush Kidane, Romanowicz, Renata, Osuch, Marzena, Szczes´niak, Mateusz, Kardel, Ignacy, Okruszko, Tomasz, Mezghani, Abdelkader, Kundzewicz, Zbigniew W.

River flow projections for two future time horizons and RCP 8.5 scenario, generated by two projects (CHASE-PL and CHIHE) in the Polish-Norwegian Research Programme, were compared. The projects employed different hydrological models over different spatial domains. The semi-distributed, process-based, SWAT model was used in the CHASE-PL project for the entire Vistula and Odra basins area, whilst the lumped, conceptual, HBV model was used in the CHIHE project for eight Polish catchments, for which the comparison study was made. Climate projections in both studies originated from the common EURO-CORDEX dataset, but they were different, e.g. due to different bias correction approaches. Increases in mean annual and seasonal flows were projected in both studies, yet the magnitudes of changes were largely different, in particular for the lowland catchments in the far future. The HBV-based increases were significantly higher in the latter case than the SWAT-based increases in all seasons except winter. Uncertainty in projections is high and creates a problem for practitioners.

2018, Kundzewicz, Zbigniew W., Piniewski, Mikołaj, Mezghani, Abdelkader, Okruszko, Tomasz, Pińskwar, Iwona, Kardel, Ignacy, Hov, Øystein, Szcześniak, Mateusz, Szwed, Małgorzata, Benestad, Rasmus E., Marcinkowski, Paweł, Graczyk, Dariusz, Dobler, Andreas, Førland, Eirik J., O’Keefe, Joanna, Choryński, Adam, Parding, Kajsa M., Haugen, Jan Erik

The present paper offers a brief assessment of climate change and associated impact in Poland, based on selected results of the Polish–Norwegian CHASE-PL project. Impacts are examined in selected sectors, such as water resources, natural hazard risk reduction, environment, agriculture and health. Results of change detection in long time series of observed climate and climate impact variables in Poland are presented. Also, projections of climate variability and change are provided for time horizons of 2021–2050 and 2071–2100 for two emission scenarios, RCP4.5 and RCP8.5 in comparison with control period, 1971–2000. Based on climate projections, examination of future impacts on sectors is also carried out. Selected uncertainty issues relevant to observations, understanding and projections are tackled as well.

2015, Lobanova, A., Stagl, J., Vetter, T., Hattermann, F.

2017, Fronzek, Stefan, Pirttioja, Nina, Carter, Timothy R., Bindi, Marco, Hoffmann, Holger, Palosuo, Taru, Ruiz-Ramos, Margarita, Tao, Fulu, Trnka, Miroslav, Acutis, Marco, Asseng, Senthold, Baranowski, Piotr, Basso, Bruno, Bodin, Per, Buis, Samuel, Cammarano, Davide, Deligios, Paola, Destain, Marie-France, Dumont, Benjamin, Ewert, Frank, Ferrise, Roberto, François, Louis, Gaiser, Thomas, Hlavinka, Petr, Jacquemin, Ingrid, Kersebaum, Kurt Christian, Kollas, Chris, Krzyszczak, Jaromir, Lorite, Ignacio J., Minet, Julien, Minguez, M. Ines, Montesino, Manuel, Moriondo, Marco, Müller, Christoph, Nendel, Claas, Öztürk, Isik, Perego, Alessia, Rodríguez, Alfredo, Ruane, Alex C., Ruget, Françoise, Sanna, Mattia, Semenov, Mikhail A., Slawinski, Cezary, Stratonovitch, Pierre, Supit, Iwan, Waha, Katharina, Wang, Enli, Wu, Lianhai, Zhao, Zhigan, Rötter, Reimund P.

Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.

2005, Hinkel, J.

Integrated modelling of global environmental change impacts faces the challenge that knowledge from the domains of Natural and Social Science must be integrated. This is complicated by often incompatible terminology and the fact that the interactions between subsystems are usually not fully understood at the start of the project. While a modular modelling approach is necessary to address these challenges, it is not sufficient. The remaining question is how the modelled system shall be cut down into modules. While no generic answer can be given to this question, communication tools can be provided to support the process of modularisation and integration. Along those lines of thought a method for building modular integrated models was developed within the EU project DINAS-COAST and applied to construct a first model, which assesses the vulnerability of the world’s coasts to climate change and sea-level-rise. The method focuses on the development of a common language and offers domain experts an intuitive interface to code their knowledge in form of modules. However, instead of rigorously defining interfaces between the subsystems at the project’s beginning, an iterative model development process is defined and tools to facilitate communication and collaboration are provided. This flexible approach has the advantage that increased understanding about subsystem interactions, gained during the project’s lifetime, can immediately be reflected in the model.

2017, Didovets, I., Lobanova, A., Bronstert, A., Snizhko, S., Maule, C.F., Krysanova, V.

The information about climate change impact on river discharge is vitally important for planning adaptation measures. The future changes can affect different water-related sectors. The main goal of this study was to investigate the potential water resource changes in Ukraine, focusing on three mesoscale river catchments (Teteriv, UpperWestern Bug, and Samara) characteristic for different geographical zones. The catchment scale watershed model-Soil and Water Integrated Model (SWIM)-was setup, calibrated, and validated for the three catchments under consideration. A set of seven GCM-RCM (General Circulation Model-Regional Climate Model) coupled climate scenarios corresponding to RCPs (Representative Concentration Pathways) 4.5 and 8.5 were used to drive the hydrological catchment model. The climate projections, used in the study, were considered as three combinations of low, intermediate, and high end scenarios. Our results indicate the shifts in the seasonal distribution of runoff in all three catchments. The spring high flow occurs earlier as a result of temperature increases and earlier snowmelt. The fairly robust trend is an increase in river discharge in the winter season, and most of the scenarios show a potential decrease in river discharge in the spring.

2018, Yalew, A.W., Hirte, G., Lotze-Campen, H., Tscharaktschiew, S.

Quantifying the economic effects of climate change is a crucial step for planning adaptation in developing countries. This study assesses the economy-wide and regional effects of climate change-induced productivity and labor supply shocks in Ethiopian agriculture. We pursue a structural approach that blends biophysical and economic models. We consider different crop yield projections and add a regionalization to the country-wide CGE results. The study shows, in the worst case scenario, the effects on country-wide GDP may add up to -8%. The effects on regional value-added GDP are uneven and range from -10% to +2.5%. However, plausible cost-free exogenous structural change scenarios in labor skills and marketing margins may offset about 20-30% of these general equilibrium effects. As such, the ongoing structural transformation in the country may underpin the resilience of the economy to climate change. This can be regarded as a co-benefit of structural change in the country. Nevertheless, given the role of the sector in the current economic structure and the potency of the projected biophysical impacts, adaptation in agriculture is imperative. Otherwise, climate change may make rural livelihoods unpredictable and strain the country's economic progress.

2012, Pohle, I., Koch, H., Grünewald, U.

Lusatia is considered one of the driest regions of Germany. The climatic water balance is negative even under current climate conditions. Due to global climate change, increased temperatures and a shift of precipitation from summer to winter are expected. Therefore, it is of major interest whether the excess water in winter can be stored and to which extent it is used up on increasing evapotranspiration. Thus, this study focuses on estimating potential climate change impacts on the water balance of two subcatchments of the River Spree using the Soil and Water Integrated Model (SWIM). Climate input was taken from 100 realisations each of two scenarios of the STatistical Analogue Resampling scheme STAR assuming a further temperature increase of 0 K (scenario A) and 2 K by the year 2055 (scenario B) respectively. Resulting from increased temperatures and a shift in precipitation from summer to winter actual evapotranspiration is supposed to increase in winter and early spring, but to decrease in later spring and early summer. This is less pronounced for scenario A than for scenario B. Consequently, also the decrease in discharge and groundwater recharge in late spring is lower for scenario A than for scenario B. The highest differences of runoff generation and groundwater recharge between the two scenarios but also the highest ranges within the scenarios occur in summer and early autumn. It is planned to estimate potential climate change for the catchments of Spree, Schwarze Elster and Lusatian Neisse.

2015, Aich, V., Liersch, S., Vetter, T., Andersson, J.C.M., Müller, E.N., Hattermann, F.F.

2017, James, R., Washington, R., Schleussner, C.-F., Rogelj, J., Conway, D.

The Paris Agreement long-term global temperature goal refers to two global warming levels: well below 2°C and 1.5°C above preindustrial. Regional climate signals at specific global warming levels, and especially the differences between 1.5°C and 2°C, are not well constrained, however. In particular, methodological challenges related to the assessment of such differences have received limited attention. This article reviews alternative approaches for identifying regional climate signals associated with global temperature limits, and evaluates the extent to which they constitute a sound basis for impacts analysis. Four methods are outlined, including comparing data from different greenhouse gas scenarios, sub-selecting climate models based on global temperature response, pattern scaling, and extracting anomalies at the time of each global temperature increment. These methods have rarely been applied to compare 2°C with 1.5°C, but some demonstrate potential avenues for useful research. Nevertheless, there are methodological challenges associated with the use of existing climate model experiments, which are generally designed to model responses to different levels of greenhouse gas forcing, rather than to model climate responses to a specific level of forcing that targets a given level of global temperature change. Novel approaches may be required to address policy questions, in particular: to differentiate between half degree warming increments while accounting for different sources of uncertainty; to examine mechanisms of regional climate change including the potential for nonlinear responses; and to explore the relevance of time-lagged processes in the climate system and declining emissions, and the resulting sensitivity to alternative mitigation pathways. WIREs Clim Change 2017, 8:e457. doi: 10.1002/wcc.457. For further resources related to this article, please visit the WIREs website.