Jason P. Evans

Discrimination between climate and human-induced dryland degradation

In this study we present a technique to discriminate between climate or human-induced dryland degradation, based on evaluations of AVHRR NDVI data and rainfall data. Since dryland areas typically have high inter-annual rainfall variations and rainfall has a dominant role in determining vegetation growth, minor biomass trends imposed by human influences are difficult to verify. By performing many linear regression calculations between different periods of accumulated precipitation and the annual NDVImax, we identify the rainfall period that is best related to the NDVImax and by this the proportion of biomass triggered by rainfall. Positive or negative deviations in biomass from this relationship, expressed in the residuals, are interpreted as human-induced. We discuss several approaches that use either a temporally fixed NDVI peaking time or an absolute one, a best mean rainfall period for the entire drylands or the best rainfall period for each individual pixel. Advantages and disadvantages of either approach or one of its combinations for discriminating between climate and human-induced degradation are discussed. Depending on the particular land-use either method has advantages. To locate areas with a high likelihood of human-induced degradation we therefore recommend combining results from each approach.

Future changes to the intensity and frequency of short‐duration extreme rainfall

Evidence that extreme rainfall intensity is increasing at the global scale has strengthened considerably in recent years. Research now indicates that the greatest increases are likely to occur in short-duration storms lasting less than a day, potentially leading to an increase in the magnitude and frequency of flash floods. This review examines the evidence for subdaily extreme rainfall intensification due to anthropogenic climate change and describes our current physical understanding of the association between subdaily extreme rainfall intensity and atmospheric temperature. We also examine the nature, quality, and quantity of information needed to allow society to adapt successfully to predicted future changes, and discuss the roles of observational and modeling studies in helping us to better understand the physical processes that can influence subdaily extreme rainfall characteristics. We conclude by describing the types of research required to produce a more thorough understanding of the relationships between local-scale thermodynamic effects, large-scale atmospheric circulation, and subdaily extreme rainfall intensity.

Evaluating the performance of a WRF physics ensemble over South-East Australia

When using the Weather Research and Forecasting (WRF) modelling system it is necessary to choose between many parametrisations for each physics option. This study examines the performance of various physics scheme combinations on the simulation of a series of rainfall events near the south-east coast of Australia known as East Coast Lows. A thirty-six member multi-physics ensemble was created such that each member had a unique set of physics parametrisations. No single ensemble member was found to perform best for all events, variables and metrics. This is reflected in the fact that different climate variables are found to be sensitive to different physical parametrisations. While a standardised super-metric can be used to identify best performers, a step-wise decision approach described here, allows explicit recognition of the “robustness” of choosing one parameterisation over another, allowing the identification of a group of “equally robustly” performing physics combinations. These results suggest that the Mellor-Yamada-Janjic planetary boundary layer scheme and the Betts-Miller-Janjic cumulus scheme can be chosen with some robustness. Possibly with greater confidence, the results also suggest that the Yonsei University planetary boundary layer scheme, Kain-Fritsch cumulus scheme and RRTMG radiation scheme should not be used in combination in this region. Results further indicate that the selection of physics scheme options has larger impact on model performance during the more intensive rainfall events.

Classifying rangeland vegetation type and coverage from NDVI time series using Fourier Filtered Cycle Similarity

We present a method for a supervised classification of Normalized Difference Vegetation Index (NDVI) time series that identifies vegetation type and vegetation coverage, absolute in %coverage or relative to a reference NDVI cycle. The shape of the NDVI cycle, which is diagnostic for certain vegetation types, is our primary classifier. A Discrete Fourier Filter is applied to time series data in order to minimize the influence of high‐frequency noise on class assignments. Similarity between filtered NDVI cycles is evaluated using a linear regression technique. The correlation coefficients calculated between the Fourier filtered reference cycle and likewise filtered target cycles describe the similarity of their phenology, and the corresponding regression coefficients are an expression of coverage relative to the reference. The regression coefficients are correlated with field measured vegetation coverage. The Fourier Filtered Cycle Similarity method (FFCS) compensates phenological shifts, which are typical in areas with a strong climate gradient, and prevents the break‐up of classes of identical vegetation types on the basis of vegetation coverage. Some other advantages compared to traditional unsupervised classifications are: synoptic visualization of vegetation type and coverage variation, independence from scene statistics, and consistent classification of biophysical characteristics only, without rock/soil reflectance dominating class assignment as it often does in unsupervised classifications of sparsely vegetated areas. Using the FFCS classification we differentiated a total of five rangeland vegetation types for the area of Syria including their intra‐class coverage variation. Classified classes are dominated by one of two shrub types, one of two annual grass types or a bare soil/sparsely vegetated type.

Temperature response to future urbanization and climate change

This study examines the impact of future urban expansion on local near-surface temperature for Sydney (Australia) using a future climate scenario (A2). The Weather Research and Forecasting model was used to simulate the present (1990–2009) and future (2040–2059) climates of the region at 2-km spatial resolution. The standard land use of the model was replaced with a more accurate dataset that covers the Sydney area. The future simulation incorporates the projected changes in the urban area of Sydney to account for the expected urban expansion. A comparison between areas with projected land use changes and their surroundings was conducted to evaluate how urbanization and global warming will act together and to ascertain their combined effect on the local climate. The analysis of the temperature changes revealed that future urbanization will strongly affect minimum temperature, whereas little impact was detected for maximum temperature. The minimum temperature changes will be noticeable throughout the year. However, during winter and spring these differences will be particularly large and the increases could be double the increase due to global warming alone at 2050. Results indicated that the changes were mostly due to increased heat capacity of urban structures and reduced evaporation in the city environment.

Development of a simple, catchment-scale, rainfall-evapotranspiration-runoff model

Representation of the hydrological interaction between the land surface and the atmosphere requires considerable improvement, particularly for predicting evapotranspiration feedbacks for use in models of the general circulation (GCMs) of the atmosphere. The predictive model developed here attempts to use a water balance approach that extracts information from the masses of catchment-scale time series data available on precipitation, energy-related variables and stream discharge. It begins with a few simple assumptions in order to seek some synthesis of the climate and landscape controls on evapotranspiration and soil moisture feedbacks, and catchment water yields. The model adopts the hydrograph identification approach used in the linear module of the rainfallrunoff model IHACRES but replaces the previous statistically based non-linear evapotranspiration loss module by a catchment moisture deficit accounting scheme. One advantage of this more conceptual approach is that evapotranspiration can be output on the same time step at which precipitation and energy variables are available (such as from GCMs), and this time step can be shorter (e.g. half hourly) than the discharge time step (e.g. daily) used to calibrate the model parameters.

Changing Climate and Overgrazing Are Decimating Mongolian Steppes

Satellite observations identify the Mongolian steppes as a hotspot of global biomass reduction, the extent of which is comparable with tropical rainforest deforestation. To conserve or restore these grasslands, the relative contributions of climate and human activities to degradation need to be understood. Here we use a recently developed 21-year (1988–2008) record of satellite based vegetation optical depth (VOD, a proxy for vegetation water content and aboveground biomass), to show that nearly all steppe grasslands in Mongolia experienced significant decreases in VOD. Approximately 60% of the VOD declines can be directly explained by variations in rainfall and surface temperature. After removing these climate induced influences, a significant decreasing trend still persists in the VOD residuals across regions of Mongolia. Correlations in spatial patterns and temporal trends suggest that a marked increase in goat density with associated grazing pressures and wild fires are the most likely non-climatic factors behind grassland degradation.

Orographic Precipitation and Water Vapor Fractionation over the Southern Andes

The climatological nature of orographic precipitation in the southern Andes between 40° and 48°S is investigated primarily using stable isotope data from streamwater. In addition, four precipitation events are examined using balloon soundings and satellite images. The Moderate Resolution Imaging Spectroradiometer (MODIS) images taken during precipitation events reveal complex patterns of upstream open-cell convection over the ocean, stratus and/or convective clouds over the mountains, and sharp leeside clearing and roll convection over the steppe. Using the water vapor bands on MODIS reveals a sharp drop in column water vapor from about 1.4 to 0.7 cm across the mountain range. Seventy-one water samples from streams across the southern Andes provide deuterium and oxygen-18 isotope data to determine the drying ratio (DR) of airstreams crossing the mountain range and to constrain free parameters in a mathematical model of orographic precipitation. From the strong isotope fractionation associated with orographic precipitation, it is estimated that DR is 50%, the highest value yet found for a mountain range. The cloud delay parameters in a high-resolution linear precipitation model were optimized to fit the streamwater isotope data. The model agrees well with the data when the cloud delay time (i.e., elapsed time from condensation to precipitation) is about 1700 s. The tuned model is used to discuss the small-scale spatial pattern of precipitation. The isotope data from streams are also compared with data from sapwater. The good agreement suggests that future isotope mapping could be done using trees.

Investigating the Mechanisms of Diurnal Rainfall Variability Using a Regional Climate Model

AbstractThis study investigates the ability of a regional climate model (RCM) to simulate the diurnal cycle of precipitation over southeast Australia, to provide a basis for understanding the mechanisms that drive diurnal variability. When compared with 195 observation gauges, the RCM tends to simulate too many occurrences and too little intensity for precipitation events at the 3-hourly time scale. However, the overall precipitation amounts are well simulated and the diurnal variability in occurrences and intensities are generally well reproduced, particularly in spring and summer. In terms of precipitation amounts, the RCM overestimated the diurnal cycle during the warmer months but was reasonably accurate during winter. The timing of the maxima and minima was found to match the observed timings well. The spatial pattern of diurnal variability in the Weather Research and Forecasting model outputs was remarkably similar to the observed record, capturing many features of regional variability. The RCM diur...

Optimally choosing small ensemble members to produce robust climate simulations

This study examines the subset climate model ensemble size required to reproduce certain statistical characteristics from a full ensemble. The ensemble characteristics examined are the root mean square error, the ensemble mean and standard deviation. Subset ensembles are created using measures that consider the simulation performance alone or include a measure of simulation independence relative to other ensemble members. It is found that the independence measure is able to identify smaller subset ensembles that retain the desired full ensemble characteristics than either of the performance based measures. It is suggested that model independence be considered when choosing ensemble subsets or creating new ensembles.