Martin Drews

A framework for testing the ability of models to project climate change and its impacts

Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections.

The role of uncertainty in climate change adaptation strategies—A Danish water management example

We propose a generic framework to characterize climate change adaptation uncertainty according to three dimensions: level, source and nature. Our framework is different, and in this respect more comprehensive, than the present UN Intergovernmental Panel on Climate Change (IPCC) approach and could be used to address concerns that the IPCC approach is oversimplified. We have studied the role of uncertainty in climate change adaptation planning using examples from four Danish water related sectors. The dominating sources of uncertainty differ greatly among issues; most uncertainties on impacts are epistemic (reducible) by nature but uncertainties on adaptation measures are complex, with ambiguity often being added to impact uncertainties. Strategies to deal with uncertainty in climate change adaptation should reflect the nature of the uncertainty sources and how they interact with risk level and decision making: (i) epistemic uncertainties can be reduced by gaining more knowledge; (ii) uncertainties related to ambiguity can be reduced by dialogue and knowledge sharing between the different stakeholders; and (iii) aleatory uncertainty is, by its nature, non-reducible. The uncertainty cascade includes many sources and their propagation through technical and socio-economic models may add substantially to prediction uncertainties, but they may also cancel each other. Thus, even large uncertainties may have small consequences for decision making, because multiple sources of information provide sufficient knowledge to justify action in climate change adaptation.

Local control on precipitation in a fully coupled climate-hydrology model.

The ability to simulate regional precipitation realistically by climate models is essential to understand and adapt to climate change. Due to the complexity of associated processes, particularly at unresolved temporal and spatial scales this continues to be a major challenge. As a result, climate simulations of precipitation often exhibit substantial biases that affect the reliability of future projections. Here we demonstrate how a regional climate model (RCM) coupled to a distributed hydrological catchment model that fully integrates water and energy fluxes between the subsurface, land surface, plant cover and the atmosphere, enables a realistic representation of local precipitation. Substantial improvements in simulated precipitation dynamics on seasonal and longer time scales is seen for a simulation period of six years and can be attributed to a more complete treatment of hydrological sub-surface processes including groundwater and moisture feedback. A high degree of local influence on the atmosphere suggests that coupled climate-hydrology models have a potential for improving climate projections and the results further indicate a diminished need for bias correction in climate-hydrology impact studies.

High-throughput screening of Erwinia chrysanthemi pectin methylesterase variants using carbohydrate microarrays.

Pectin methylesterases (PMEs) catalyse the removal of methyl esters from the homogalacturonan (HG) backbone domain of pectin, a ubiquitous polysaccharide in plant cell walls. The degree of methyl esterification (DE) impacts upon the functional properties of HG within cell walls and plants produce numerous PMEs that act upon HG in muro. Many microbial plant pathogens also produce PMEs, the activity of which renders HG more susceptible to cleavage by pectin lyase and polygalacturonase enzymes and hence aids cell wall degradation. We have developed a novel microarray‐based approach to investigate the activity of a series of variant enzymes based on the PME from the important pathogen Erwinia chrysanthemi. A library of 99 E. chrysanthemi PME mutants was created in which seven amino acids were altered by various different substitutions. Each mutant PME was incubated with a highly methyl esterified lime pectin substrate and, after digestion the enzyme/substrate mixtures were printed as microarrays. The loss of activity that resulted from certain mutations was detected by probing arrays with a mAb (JIM7) that preferentially binds to HG with a relatively high DE. Active PMEs therefore resulted in diminished JIM7 binding to the lime pectin substrate, whereas inactive PMEs did not. Our findings demonstrate the feasibility of our approach for rapidly testing the effects on PME activity of substituting a wide variety of amino acids at different positions.

Spatial-Scale Characteristics of Precipitation Simulated by Regional Climate Models and the Implications for Hydrological Modeling

AbstractPrecipitation simulated by regional climate models (RCMs) is generally biased with respect to observations, especially at the local scale of a few tens of kilometers. This study investigates how well two different RCMs are able to reproduce the spatial correlation patterns of observed summer precipitation for the central United States. On local scales, gridded precipitation observations and simulated precipitation are compared for the period of the 1987 First International Satellite Land Surface Climatological Project Field Experiment (FIFE) campaign. The results show that spatial correlation length scales on the order of 130 km are found in both observed data and RCM simulations. When simulations and observations are aggregated to different grid sizes, the pattern correlation significantly decreases when the aggregation length is less than roughly 100 km. Furthermore, the intermodel standard deviation between simulations with different domains or resolutions increases for aggregation lengths belo...

Using Landsat Vegetation Indices to Estimate Impervious Surface Fractions for European Cities

Impervious surfaces (IS) are a key indicator of environmental quality, and mapping of urban IS is important for a wide range of applications including hydrological modelling, water management, urban and environmental planning and urban climate studies. This paper addresses the accuracy and applicability of vegetation indices (VI), from Landsat imagery, to estimate IS fractions for European cities. The accuracy of three different measures of vegetation cover is examined for eight urban areas at different locations in Europe. The Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are converted to IS fractions using a regression modelling approach. Also, NDVI is used to estimate fractional vegetation cover (FR), and consequently IS fractions. All three indices provide fairly accurate estimates (MAEs ≈ 10%, MBE’s < 2%) of sub-pixel imperviousness, and are found to be applicable for cities with dissimilar climatic and vegetative conditions. The VI/IS relationship across cities is examined by quantifying the MAEs and MBEs between all combinations of models and urban areas. Also, regional regression models are developed by compiling data from multiple cities to examine the potential for developing and applying a single regression model to estimate IS fractions for numerous urban areas without reducing the accuracy considerably. Our findings indicate that the models can be applied broadly for multiple urban areas, and that the accuracy is reduced only marginally by applying the regional models. SAVI is identified as a superior index for the development of regional quantification models. The findings of this study highlight that IS fractions, and spatiotemporal changes herein, can be mapped by use of simple regression models based on VIs from remote sensors, and that the method presented enables simple, accurate and resource efficient quantification of IS.

Atmospheric dispersion of argon-41 from a nuclear research reactor: measurement and modelling of plume geometry and gamma radiation field

An atmospheric dispersion experiment was conducted using a visible tracer along with the routine releases of 41Ar from the BR1 research reactor in Mol, Belgium. Simultaneous measurements of plume geometry and radiation field from 41Ar decay were performed as well as measurements of the 41Ar source term and the meteorological parameters. Good overall agreement is found between measurement data and model results using the mesoscale atmospheric dispersion and dose rate model RIMPUFF.

Climate change impacts on groundwater hydrology – where are the main uncertainties and can they be reduced?

ABSTRACT This paper assesses how various sources of uncertainty propagate through the uncertainty cascade from emission scenarios through climate models and hydrological models to impacts, with a particular focus on groundwater aspects from a number of coordinated studies in Denmark. Our results are similar to those from surface water studies showing that climate model uncertainty dominates the results for projections of climate change impacts on streamflow and groundwater heads. However, we found uncertainties related to geological conceptualization and hydrological model discretization to be dominant for projections of well field capture zones, while the climate model uncertainty here is of minor importance. How to reduce the uncertainties on climate change impact projections related to groundwater is discussed, with an emphasis on the potential for reducing climate model biases through the use of fully coupled climate–hydrology models. Editor D. Koutsoyiannis; Associate editor not assigned

Simulation of Optimal Decision-Making Under the Impacts of Climate Change

Climate change causes transformations to the conditions of existing agricultural practices appointing farmers to continuously evaluate their agricultural strategies, e.g., towards optimising revenue. In this light, this paper presents a framework for applying Bayesian updating to simulate decision-making, reaction patterns and updating of beliefs among farmers in a developing country, when faced with the complexity of adapting agricultural systems to climate change. We apply the approach to a case study from Ghana, where farmers seek to decide on the most profitable of three agricultural systems (dryland crops, irrigated crops and livestock) by a continuous updating of beliefs relative to realised trajectories of climate (change), represented by projections of temperature and precipitation. The climate data is based on combinations of output from three global/regional climate model combinations and two future scenarios (RCP4.5 and RCP8.5) representing moderate and unsubstantial greenhouse gas reduction policies, respectively. The results indicate that the climate scenario (input) holds a significant influence on the development of beliefs, net revenues and thereby optimal farming practices. Further, despite uncertainties in the underlying net revenue functions, the study shows that when the beliefs of the farmer (decision-maker) opposes the development of the realised climate, the Bayesian methodology allows for simulating an adjustment of such beliefs, when improved information becomes available. The framework can, therefore, help facilitating the optimal choice between agricultural systems considering the influence of climate change.

Sea level adaptation decisions under uncertainty

Sea level rise has serious consequences for harbor infrastructure, storm drains and sewer systems, and many other issues. Adapting to sea level rise requires comparing different possible adaptation strategies, comparing the cost of different actions (including no action), and assessing where and at what point in time the chosen strategy should be implemented. All these decisions must be made under considerable uncertainty—in the amount of sea level rise, in the cost and prioritization of adaptation actions, and in the implications of no action. Here we develop two illustrative examples: for Bergen on Norways west coast and for Esbjerg on the west coast of Denmark, to highlight how technical efforts to understand and quantify uncertainties in hydrologic projections can be coupled with concrete decision-problems framed by the needs of the end-users using statistical formulations. Different components of uncertainty are visualized. We demonstrate the value of uncertainties and show for example that failing to take uncertainty into account can result in the median-projected damage costs being an order of magnitude smaller.