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The most frequently used boundary-layer turbulence parameterization in numerical weather prediction (NWP) models are turbulence kinetic energy (TKE) based-based schemes. However, these parameterizations suffer from a potential weakness, namely the strong dependence on an ad-hoc quantity, the so-called turbulence length scale. The physical interpretation of the turbulence length scale is difficult and hence it cannot be directly related to measurements or large eddy simulation (LES) data. Consequently, formulations for the turbulence length scale in basically all TKE schemes are based on simplified assumptions and are model-dependent. A good reference for the independent evaluation of the turbulence length scale expression for NWP modeling is missing. Here we propose a new turbulence length scale diagnostic which can be used in the gray zone of turbulence without modifying the underlying TKE turbulence scheme. The new diagnostic is based on the TKE budget: The core idea is to encapsulate the sum of the molecular dissipation and the cross-scale TKE transfer into an effective dissipation, and associate it with the new turbulence length scale. This effective dissipation can then be calculated as a residuum in the TKE budget equation (for horizontal sub-domains of different sizes) using LES data. Estimation of the scale dependence of the diagnosed turbulence length scale using this novel method is presented for several idealized cases.
Aim: Predicting future changes in species richness in response to climate change is one of the key challenges in biogeography and conservation ecology. Stacked species distribution models (S‐SDMs) are a commonly used tool to predict current and future species richness. Macroecological models (MEMs), regression models with species richness as response variable, are a less computationally intensive alternative to S‐SDMs. Here, we aim to compare the results of two model types (S‐SDMS and MEMs), for the first time for more than 14,000 species across multiple taxa globally, and to trace the uncertainty in future predictions back to the input data and modelling approach used.
Location: Global land, excluding Antarctica.
Taxon: Amphibians, birds and mammals.
Methods: We fitted S‐SDMs and MEMs using a consistent set of bioclimatic variables and model algorithms and conducted species richness predictions under current and future conditions. For the latter, we used four general circulation models (GCMs) under two representative concentration pathways (RCP2.6 and RCP6.0). Predicted species richness was compared between S‐SDMs and MEMs and for current conditions also to extent‐of‐occurrence (EOO) species richness patterns. For future predictions, we quantified the variance in predicted species richness patterns explained by the choice of model type, model algorithm and GCM using hierarchical cluster analysis and variance partitioning.
Results: Under current conditions, species richness predictions from MEMs and S‐SDMs were strongly correlated with EOO‐based species richness. However, both model types over‐predicted areas with low and under‐predicted areas with high species richness. Outputs from MEMs and S‐SDMs were also highly correlated among each other under current and future conditions. The variance between future predictions was mostly explained by model type.
Main conclusions: Both model types were able to reproduce EOO‐based patterns in global terrestrial vertebrate richness, but produce less collinear predictions of future species richness. Model type by far contributes to most of the variation in the different future species richness predictions, indicating that the two model types should not be used interchangeably. Nevertheless, both model types have their justification, as MEMs can also include species with a restricted range, whereas S‐SDMs are useful for looking at potential species‐specific responses.
Immersion freezing is the most relevant heterogeneous ice nucleation mechanism through which ice crystals are formed in mixed-phase clouds. In recent years, an increasing number of laboratory experiments utilizing a variety of instruments have examined immersion freezing activity of atmospherically relevant ice-nucleating particles. However, an intercomparison of these laboratory results is a difficult task because investigators have used different ice nucleation (IN) measurement methods to produce these results. A remaining challenge is to explore the sensitivity and accuracy of these techniques and to understand how the IN results are potentially influenced or biased by experimental parameters associated with these techniques.
Within the framework of INUIT (Ice Nuclei Research Unit), we distributed an illite-rich sample (illite NX) as a representative surrogate for atmospheric mineral dust particles to investigators to perform immersion freezing experiments using different IN measurement methods and to obtain IN data as a function of particle concentration, temperature (T), cooling rate and nucleation time. A total of 17 measurement methods were involved in the data intercomparison. Experiments with seven instruments started with the test sample pre-suspended in water before cooling, while 10 other instruments employed water vapor condensation onto dry-dispersed particles followed by immersion freezing. The resulting comprehensive immersion freezing data set was evaluated using the ice nucleation active surface-site density, ns, to develop a representative ns(T) spectrum that spans a wide temperature range (−37 °C < T < −11 °C) and covers 9 orders of magnitude in ns.
In general, the 17 immersion freezing measurement techniques deviate, within a range of about 8 °C in terms of temperature, by 3 orders of magnitude with respect to ns. In addition, we show evidence that the immersion freezing efficiency expressed in ns of illite NX particles is relatively independent of droplet size, particle mass in suspension, particle size and cooling rate during freezing. A strong temperature dependence and weak time and size dependence of the immersion freezing efficiency of illite-rich clay mineral particles enabled the ns parameterization solely as a function of temperature. We also characterized the ns(T) spectra and identified a section with a steep slope between −20 and −27 °C, where a large fraction of active sites of our test dust may trigger immersion freezing. This slope was followed by a region with a gentler slope at temperatures below −27 °C. While the agreement between different instruments was reasonable below ~ −27 °C, there seemed to be a different trend in the temperature-dependent ice nucleation activity from the suspension and dry-dispersed particle measurements for this mineral dust, in particular at higher temperatures. For instance, the ice nucleation activity expressed in ns was smaller for the average of the wet suspended samples and higher for the average of the dry-dispersed aerosol samples between about −27 and −18 °C. Only instruments making measurements with wet suspended samples were able to measure ice nucleation above −18 °C. A possible explanation for the deviation between −27 and −18 °C is discussed. Multiple exponential distribution fits in both linear and log space for both specific surface area-based ns(T) and geometric surface area-based ns(T) are provided. These new fits, constrained by using identical reference samples, will help to compare IN measurement methods that are not included in the present study and IN data from future IN instruments.
Immersion freezing is the most relevant heterogeneous ice nucleation mechanism through which ice crystals are formed in mixed-phase clouds. In recent years, an increasing number of laboratory experiments utilizing a variety of instruments have examined immersion freezing activity of atmospherically relevant ice nucleating particles (INPs). However, an inter-comparison of these laboratory results is a difficult task because investigators have used different ice nucleation (IN) measurement methods to produce these results. A remaining challenge is to explore the sensitivity and accuracy of these techniques and to understand how the IN results are potentially influenced or biased by experimental parameters associated with these techniques.
Within the framework of INUIT (Ice Nucleation research UnIT), we distributed an illite rich sample (illite NX) as a representative surrogate for atmospheric mineral dust particles to investigators to perform immersion freezing experiments using different IN measurement methods and to obtain IN data as a function of particle concentration, temperature (T), cooling rate and nucleation time. Seventeen measurement methods were involved in the data inter-comparison. Experiments with seven instruments started with the test sample pre-suspended in water before cooling, while ten other instruments employed water vapor condensation onto dry-dispersed particles followed by immersion freezing. The resulting comprehensive immersion freezing dataset was evaluated using the ice nucleation active surface-site density (ns) to develop a representative ns(T) spectrum that spans a wide temperature range (−37 °C < T < −11 °C) and covers nine orders of magnitude in ns.
Our inter-comparison results revealed a discrepancy between suspension and dry-dispersed particle measurements for this mineral dust. While the agreement was good below ~ −26 °C, the ice nucleation activity, expressed in ns, was smaller for the wet suspended samples and higher for the dry-dispersed aerosol samples between about −26 and −18 °C. Only instruments making measurement techniques with wet suspended samples were able to measure ice nucleation above −18 °C. A possible explanation for the deviation between −26 and −18 °C is discussed. In general, the seventeen immersion freezing measurement techniques deviate, within the range of about 7 °C in terms of temperature, by three orders of magnitude with respect to ns. In addition, we show evidence that the immersion freezing efficiency (i.e., ns) of illite NX particles is relatively independent on droplet size, particle mass in suspension, particle size and cooling rate during freezing. A strong temperature-dependence and weak time- and size-dependence of immersion freezing efficiency of illite-rich clay mineral particles enabled the ns parameterization solely as a function of temperature. We also characterized the ns (T) spectra, and identified a section with a steep slope between −20 and −27 °C, where a large fraction of active sites of our test dust may trigger immersion freezing. This slope was followed by a region with a gentler slope at temperatures below −27 °C. A multiple exponential distribution fit is expressed as ns(T) = exp(23.82 × exp(−exp(0.16 × (T + 17.49))) + 1.39) based on the specific surface area and ns(T) = exp(25.75 × exp(−exp(0.13 × (T + 17.17))) + 3.34) based on the geometric area (ns and T in m−2 and °C, respectively). These new fits, constrained by using an identical reference samples, will help to compare IN measurement methods that are not included in the present study and, thereby, IN data from future IN instruments.
An easy-to-use model to evaluate conductivities at high and middle latitudes in the height range 70–100 km is presented. It is based on electron density profiles obtained with the EISCAT VHF radar during 11 years and on the neutral atmospheric model MSIS95. The model uses solar zenith angle, geomagnetic activity and season as input parameters. It was mainly constructed to study the properties of Schumann resonances that depend on such conductivity profiles.
Vegetation responds to drought through a complex interplay of plant hydraulic mechanisms, posing challenges for model development and parameterization. We present a mathematical model that describes the dynamics of leaf water-potential over time while considering different strategies by which plant species regulate their water-potentials. The model has two parameters: the parameter λ describing the adjustment of the leaf water potential to changes in soil water potential, and the parameter Δψww describing the typical ‘well-watered’ leaf water potentials at non-stressed (near-zero) levels of soil water potential. Our model was tested and calibrated on 110 time-series datasets containing the leaf- and soil water potentials of 66 species under drought and non-drought conditions. Our model successfully reproduces the measured leaf water potentials over time based on three different regulation strategies under drought. We found that three parameter sets derived from the measurement data reproduced the dynamics of 53% of an drought dataset, and 52% of a control dataset [root mean square error (RMSE) < 0.5 MPa)]. We conclude that, instead of quantifying water-potential-regulation of different plant species by complex modeling approaches, a small set of parameters may be sufficient to describe the water potential regulation behavior for large-scale modeling. Thus, our approach paves the way for a parsimonious representation of the full spectrum of plant hydraulic responses to drought in dynamic vegetation models.
Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impact-model setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making.
Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making.
Irrigation intensifies land use by increasing crop yield but also impacts water resources. It affects water and energy balances and consequently the microclimate in irrigated regions. Therefore, knowledge of the extent of irrigated land is important for hydrological and crop modelling, global change research, and assessments of resource use and management. Information on the historical evolution of irrigated lands is limited. The new global historical irrigation data set (HID) provides estimates of the temporal development of the area equipped for irrigation (AEI) between 1900 and 2005 at 5 arcmin resolution. We collected sub-national irrigation statistics from various sources and found that the global extent of AEI increased from 63 million ha (Mha) in 1900 to 111 Mha in 1950 and 306 Mha in 2005. We developed eight gridded versions of time series of AEI by combining sub-national irrigation statistics with different data sets on the historical extent of cropland and pasture. Different rules were applied to maximize consistency of the gridded products to sub-national irrigation statistics or to historical cropland and pasture data sets. The HID reflects very well the spatial patterns of irrigated land as shown on historical maps for the western United States (around year 1900) and on a global map (around year 1960). Mean aridity on irrigated land increased and mean natural river discharge on irrigated land decreased from 1900 to 1950 whereas aridity decreased and river discharge remained approximately constant from 1950 to 2005. The data set and its documentation are made available in an open-data repository at https://mygeohub.org/publications/8 (doi:10.13019/M20599).