2014, Stagl, J., Mayr, E., Koch, H., Hattermann, F.F., Huang, S.

For the management of protected areas knowledge about the water regime plays a very important role, in particular in areas with lakes, wetlands, marches or floodplains. The local hydrological conditions depend widely on temporal and spatial variations of the main components of the hydrologic cycle and physiographic conditions on site. To preserve a favourable conservation status under changing climatic conditions park managers require information about potential impacts of climate change in their area. The following chapter provides an overview of how climate change affects the hydrological regimes in Central and Eastern Europe. The hydrological impacts for the protected areas are area-specific and vary from region to region. Generally, an increase in temperature enhances the moisture holding capacity of the atmosphere and thus, leads to an intensification of the hydrological cycle. Key changes in the hydrological system include alterations in the seasonal distribution, magnitude and duration of precipitation and evapotranspiration. This may lead to changes in the water storage, surface runoff, soil moisture and seasonal snow packs as well as to modifications in the mass balance of Central European glaciers. Partly, water resources management can help to counterbalance effects of climate change on stream flow and water availability.

2014, Zscheischler, J., Reichstein, M., Harmeling, S., Rammig, A., Tomelleri, E., Mahecha, M.D.

Climate extremes can affect the functioning of terrestrial ecosystems, for instance via a reduction of the photosynthetic capacity or alterations of respiratory processes. Yet the dominant regional and seasonal effects of hydrometeorological extremes are still not well documented and in the focus of this paper. Specifically, we quantify and characterize the role of large spatiotemporal extreme events in gross primary production (GPP) as triggers of continental anomalies. We also investigate seasonal dynamics of extreme impacts on continental GPP anomalies. We find that the 50 largest positive extremes (i.e., statistically unusual increases in carbon uptake rates) and negative extremes (i.e., statistically unusual decreases in carbon uptake rates) on each continent can explain most of the continental variation in GPP, which is in line with previous results obtained at the global scale. We show that negative extremes are larger than positive ones and demonstrate that this asymmetry is particularly strong in South America and Europe. Our analysis indicates that the overall impacts and the spatial extents of GPP extremes are power-law distributed with exponents that vary little across continents. Moreover, we show that on all continents and for all data sets the spatial extents play a more important role for the overall impact of GPP extremes compared to the durations or maximal GPP. An analysis of possible causes across continents indicates that most negative extremes in GPP can be attributed clearly to water scarcity, whereas extreme temperatures play a secondary role. However, for Europe, South America and Oceania we also identify fire as an important driver. Our findings are consistent with remote sensing products. An independent validation against a literature survey on specific extreme events supports our results to a large extent.

2014, O'Neill, B.C., Kriegler, E., Riahi, K., Ebi, K.L., Hallegatte, S., Carter, T.R., Mathur, R., van Vuuren, D.P.

The new scenario framework for climate change research envisions combining pathways of future radiative forcing and their associated climate changes with alternative pathways of socioeconomic development in order to carry out research on climate change impacts, adaptation, and mitigation. Here we propose a conceptual framework for how to define and develop a set of Shared Socioeconomic Pathways (SSPs) for use within the scenario framework. We define SSPs as reference pathways describing plausible alternative trends in the evolution of society and ecosystems over a century timescale, in the absence of climate change or climate policies. We introduce the concept of a space of challenges to adaptation and to mitigation that should be spanned by the SSPs, and discuss how particular trends in social, economic, and environmental development could be combined to produce such outcomes. A comparison to the narratives from the scenarios developed in the Special Report on Emissions Scenarios (SRES) illustrates how a starting point for developing SSPs can be defined. We suggest initial development of a set of basic SSPs that could then be extended to meet more specific purposes, and envision a process of application of basic and extended SSPs that would be iterative and potentially lead to modification of the original SSPs themselves.

2014, Feldhoff, Jan H., Lange, Stefan, Volkholz, Jan, Donges, Jonathan F., Kurths, Jürgen, Gerstengarbe, Friedrich-Wilhelm

In this study we introduce two new node-weighted difference measures on complex networks as a tool for climate model evaluation. The approach facilitates the quantification of a model’s ability to reproduce the spatial covariability structure of climatological time series. We apply our methodology to compare the performance of a statistical and a dynamical regional climate model simulating the South American climate, as represented by the variables 2 m temperature, precipitation, sea level pressure, and geopotential height field at 500 hPa. For each variable, networks are constructed from the model outputs and evaluated against a reference network, derived from the ERA-Interim reanalysis, which also drives the models. We compare two network characteristics, the (linear) adjacency structure and the (nonlinear) clustering structure, and relate our findings to conventional methods of model evaluation. To set a benchmark, we construct different types of random networks and compare them alongside the climate model networks. Our main findings are: (1) The linear network structure is better reproduced by the statistical model statistical analogue resampling scheme (STARS) in summer and winter for all variables except the geopotential height field, where the dynamical model CCLM prevails. (2) For the nonlinear comparison, the seasonal differences are more pronounced and CCLM performs almost as well as STARS in summer (except for sea level pressure), while STARS performs better in winter for all variables.

2014, Zemp, D.C., Schleussner, C.-F., Barbosa, H.M.J., van der Ent, R.J., Donges, J.F., Heinke, J., Sampaio, G., Rammig, A.

Continental moisture recycling is a crucial process of the South American climate system. In particular, evapotranspiration from the Amazon basin contributes substantially to precipitation regionally as well as over other remote regions such as the La Plata basin. Here we present an in-depth analysis of South American moisture recycling mechanisms. In particular, we quantify the importance of cascading moisture recycling (CMR), which describes moisture transport between two locations on the continent that involves re-evaporation cycles along the way. Using an Eulerian atmospheric moisture tracking model forced by a combination of several historical climate data sets, we were able to construct a complex network of moisture recycling for South America. Our results show that CMR contributes about 9–10% to the total precipitation over South America and 17–18% over the La Plata basin. CMR increases the fraction of total precipitation over the La Plata basin that originates from the Amazon basin from 18–23 to 24–29% during the wet season. We also show that the south-western part of the Amazon basin is not only a direct source of rainfall over the La Plata basin, but also a key intermediary region that distributes moisture originating from the entire Amazon basin towards the La Plata basin during the wet season. Our results suggest that land use change in this region might have a stronger impact on downwind rainfall than previously thought. Using complex network analysis techniques, we find the eastern side of the sub-tropical Andes to be a key region where CMR pathways are channeled. This study offers a better understanding of the interactions between the vegetation and the atmosphere on the water cycle, which is needed in a context of land use and climate change in South America.

2014, Willeit, M., Ganopolski, A., Feulner, G.

Climate–vegetation feedback has the potential to significantly contribute to climate change, but little is known about its range of uncertainties. Here, using an Earth system model of intermediate complexity we address possible uncertainties in the strength of the biogeophysical climate–vegetation feedback using a single-model multi-physics ensemble. Equilibrium experiments with halving (140 ppm) and doubling (560 ppm) of CO2 give a contribution of the vegetation–climate feedback to global temperature change in the range −0.3 to −0.1 °C and −0.1 to 0.2 °C, respectively. There is an asymmetry between warming and cooling, with a larger, positive vegetation–climate feedback in the lower CO2 climate. Hotspots of climate–vegetation feedback are the boreal zone, the Amazon rainforest and the Sahara. Albedo parameterization is the dominant source of uncertainty in the subtropics and at high northern latitudes, while uncertainties in evapotranspiration are more relevant in the tropics. We analyse the separate impact of changes in stomatal conductance, leaf area index and vegetation dynamics on climate and we find that different processes are dominant in lower and higher CO2 worlds. The reduction in stomatal conductance gives the main contribution to temperature increase for a doubling of CO2, while dynamic vegetation is the dominant process in the CO2 halving experiments. Globally the climate–vegetation feedback is rather small compared to the sum of the fast climate feedbacks. However, it is comparable to the amplitude of the fast feedbacks at high northern latitudes where it can contribute considerably to polar amplification. The uncertainties in the climate–vegetation feedback are comparable to the multi-model spread of the fast climate feedbacks.

2015, Willeit, M., Ganopolski, A.

Permafrost influences a number of processes which are relevant for local and global climate. For example, it is well known that permafrost plays an important role in global carbon and methane cycles. Less is known about the interaction between permafrost and ice sheets. In this study a permafrost module is included in the Earth system model CLIMBER-2, and the coupled Northern Hemisphere (NH) permafrost–ice-sheet evolution over the last glacial cycle is explored. The model performs generally well at reproducing present-day permafrost extent and thickness. Modeled permafrost thickness is sensitive to the values of ground porosity, thermal conductivity and geothermal heat flux. Permafrost extent at the Last Glacial Maximum (LGM) agrees well with reconstructions and previous modeling estimates. Present-day permafrost thickness is far from equilibrium over deep permafrost regions. Over central Siberia and the Arctic Archipelago permafrost is presently up to 200–500 m thicker than it would be at equilibrium. In these areas, present-day permafrost depth strongly depends on the past climate history and simulations indicate that deep permafrost has a memory of surface temperature variations going back to at least 800 ka. Over the last glacial cycle permafrost has a relatively modest impact on simulated NH ice sheet volume except at LGM, when including permafrost increases ice volume by about 15 m sea level equivalent in our model. This is explained by a delayed melting of the ice base from below by the geothermal heat flux when the ice sheet sits on a porous sediment layer and permafrost has to be melted first. Permafrost affects ice sheet dynamics only when ice extends over areas covered by thick sediments, which is the case at LGM.

2015, Rammig, A., Wiedermann, M., Donges, J.F., Babst, F., von Bloh, W., Frank, D., Thonicke, K., Mahecha, M.D.

Climate extremes can trigger exceptional responses in terrestrial ecosystems, for instance by altering growth or mortality rates. Such effects are often manifested in reductions in net primary productivity (NPP). Investigating a Europe-wide network of annual radial tree growth records confirms this pattern: we find that 28% of tree ring width (TRW) indices are below two standard deviations in years in which extremely low precipitation, high temperatures or the combination of both noticeably affect tree growth. Based on these findings, we investigate possibilities for detecting climate-driven patterns in long-term TRW data to evaluate state-of-the-art dynamic vegetation models such as the Lund-Potsdam-Jena dynamic global vegetation model for managed land (LPJmL). The major problem in this context is that LPJmL simulates NPP but not explicitly the radial tree growth, and we need to develop a generic method to allow for a comparison between simulated and observed response patterns. We propose an analysis scheme that quantifies the coincidence rate of climate extremes with some biotic responses (here TRW or simulated NPP). We find a relative reduction of 34% in simulated NPP during precipitation, temperature and combined extremes. This reduction is comparable to the TRW response patterns, but the model responds much more sensitively to drought stress. We identify 10 extreme years during the 20th century during which both model and measurements indicate high coincidence rates across Europe. However, we detect substantial regional differences in simulated and observed responses to climatic extreme events. One explanation for this discrepancy could be the tendency of tree ring data to originate from climatically stressed sites. The difference between model and observed data is amplified by the fact that dynamic vegetation models are designed to simulate mean ecosystem responses on landscape or regional scales. We find that both simulation results and measurements display carry-over effects from climate anomalies during the previous year. We conclude that radial tree growth chronologies provide a suitable basis for generic model benchmarks. The broad application of coincidence analysis in generic model benchmarks along with an increased availability of representative long-term measurements and improved process-based models will refine projections of the long-term carbon balance in terrestrial ecosystems.

2014, Forkel, M., Carvalhais, N., Schaphoff, S., v. Bloh, W., Migliavacca, M., Thurner, M., Thonicke, K.

Existing dynamic global vegetation models (DGVMs) have a limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and description of the environmental controls on phenology, which strongly influence the ability to simulate longer-term vegetation dynamics, e.g. carbon allocation. Combining DGVMs with observational data sets can potentially help to revise current modelling approaches and thus enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a new phenology model within the LPJmL (Lund Potsdam Jena managed lands) DGVM and integrated several observational data sets to improve the ability of the model in reproducing satellite-derived time series of vegetation greenness. Specifically, we optimized LPJmL parameters against observational time series of the fraction of absorbed photosynthetic active radiation (FAPAR), albedo and gross primary production to identify the main environmental controls for seasonal vegetation greenness dynamics. We demonstrated that LPJmL with new phenology and optimized parameters better reproduces seasonality, inter-annual variability and trends of vegetation greenness. Our results indicate that soil water availability is an important control on vegetation phenology not only in water-limited biomes but also in boreal forests and the Arctic tundra. Whereas water availability controls phenology in water-limited ecosystems during the entire growing season, water availability co-modulates jointly with temperature the beginning of the growing season in boreal and Arctic regions. Additionally, water availability contributes to better explain decadal greening trends in the Sahel and browning trends in boreal forests. These results emphasize the importance of considering water availability in a new generation of phenology modules in DGVMs in order to correctly reproduce observed seasonal-to-decadal dynamics of vegetation greenness.

2014, Rehfeld, K., Kurths, J.

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60–55% (in the linear case) to 53–42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity contributes less, particularly for the adapted Gaussian-kernel-based estimators and the event synchronization function. The introduced link strength concept summarizes the hypothesis test results and balances the individual strengths of the estimators: while gXCF is particularly suitable for short and irregular time series, gMI and the ESF can identify nonlinear dependencies. ESF could, in particular, be suitable to study extreme event dynamics in paleoclimate records. Programs to analyze paleoclimatic time series for significant dependencies are included in a freely available software toolbox.