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Jason P. Evans and Seth Westra

Abstract

This 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 diurnal cycle was dominated by the convective (subgrid scale) precipitation. In the RCM the diurnal cycle of convective precipitation over land corresponds well to atmospheric instability and thermally triggered convection over large areas, and also to the large-scale moisture convergence at 700 hPa along the east coast, with the strongest diurnal cycles present where these three mechanisms are in phase.

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Mark Decker, Andy J. Pitman, and Jason Evans

Abstract

The feasibility of using vegetation greenness metrics as a proxy for transpiration variability over Australia is demonstrated. Several global evapotranspiration datasets, one of which provides transpiration data and is constructed independently of the vegetation greenness measurements, are compared to four satellite-based observations representative of the state of the vegetation over several regions in Australia. Further estimates of the transpiration are obtained by decomposing the evapotranspiration datasets using an ensemble of land surface model simulations. On monthly time scales, the greenness anomaly metrics show a near one-to-one relationship with the transpiration estimates when the time series are appropriately scaled by the mean. The authors demonstrate that anomalous vegetation greenness metrics, when properly scaled, provide a tool for evaluating transpiration variability simulated by land surface models and observation-based evapotranspiration datasets that include transpiration. These methods provide a new test to help constrain the dynamic behavior of the land surface in climate model simulations.

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Ronald B. Smith and Jason P. Evans

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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.

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Eytan Rocheta, Jason P. Evans, and Ashish Sharma

Abstract

Global climate model simulations inherently contain multiple biases that, when used as boundary conditions for regional climate models, have the potential to produce poor downscaled simulations. Removing these biases before downscaling can potentially improve regional climate change impact assessment. In particular, reducing the low-frequency variability biases in atmospheric variables as well as modeled rainfall is important for hydrological impact assessment, predominantly for the improved simulation of floods and droughts. The impact of this bias in the lateral boundary conditions driving the dynamical downscaling has not been explored before. Here the use of three approaches for correcting the lateral boundary biases including mean, variance, and modification of sample moments through the use of a nested bias correction (NBC) method that corrects for low-frequency variability bias is investigated. These corrections are implemented at the 6-hourly time scale on the global climate model simulations to drive a regional climate model over the Australian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain. The results show that the most substantial improvement in low-frequency variability after bias correction is obtained from modifying the mean field, with smaller changes attributed to the variance. Explicitly modifying monthly and annual lag-1 autocorrelations through NBC does not substantially improve low-frequency variability attributes of simulated precipitation in the regional model over a simpler mean bias correction. These results raise questions about the nature of bias correction techniques that are required to successfully gain improvement in regional climate model simulations and show that more complicated techniques do not necessarily lead to more skillful simulation.

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Sanaa Hobeichi, Gab Abramowitz, and Jason Evans

Abstract

Accurate estimates of terrestrial water and energy cycle components are needed to better understand climate processes and improve models’ ability to simulate future change. Various observational estimates are available for the individual budget terms; however, these typically show inconsistencies when combined in a budget. In this work, a Conserving Land–Atmosphere Synthesis Suite (CLASS) of estimates of simultaneously balanced surface water and energy budget components is developed. Individual CLASS variable datasets, where possible, 1) combine a range of existing variable product estimates, and hence overcome the limitations of estimates from a single source; 2) are observationally constrained with in situ measurements; 3) have uncertainty estimates that are consistent with their agreement with in situ observations; and 4) are consistent with each other by being able to solve the water and energy budgets simultaneously. First, available datasets of a budget variable are merged by implementing a weighting method that accounts both for the ability of datasets to match in situ measurements and the error covariance between datasets. Then, the budget terms are adjusted by applying an objective variational data assimilation technique (DAT) that enforces the simultaneous closure of the surface water and energy budgets linked through the equivalence of evapotranspiration and latent heat. Comparing component estimates before and after applying the DAT against in situ measurements of energy fluxes and streamflow showed that modified estimates agree better with in situ observations across various metrics, but also revealed some inconsistencies between water budget terms in June over the higher latitudes. CLASS variable estimates are freely available via https://doi.org/10.25914/5c872258dc183.

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Jason E. Smerdon, Alexey Kaplan, Diana Chang, and Michael N. Evans

Abstract

Canonical correlation analysis (CCA) is evaluated for paleoclimate field reconstructions in the context of pseudoproxy experiments assembled from the millennial integration (850–1999 c.e.) of the National Center for Atmospheric Research Community Climate System Model, version 1.4. A parsimonious method for selecting the order of the CCA model is presented. Results suggest that the method is capable of resolving multiple (3–13) climatic patterns given the estimated proxy observational network and the amount of observational uncertainty. CCA reconstructions are compared to those derived from the regularized expectation maximization method using ridge regression regularization (RegEM-Ridge). CCA and RegEM-Ridge yield similar skill patterns that are characterized by high correlation regions collocated with dense pseudoproxy sampling areas in North America and Europe. Both methods also produce reconstructions characterized by spatially variable warm biases and variance losses, particularly at high pseudoproxy noise levels. RegEM-Ridge in particular is subject to significantly larger variance losses than CCA, even though the spatial correlation patterns of the two methods are comparable. Results collectively indicate the importance of evaluating the field performance of methods that target spatial climate patterns during the last several millennia and indicate that the results of currently available climate field reconstructions should be interpreted carefully.

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Hooman Ayat, Jason P. Evans, Steven Sherwood, and Ali Behrangi

Abstract

High-resolution datasets offer the potential to improve our understanding of spatial and temporal precipitation patterns and storm structures. The goal of this study is to evaluate the similarities and differences of object-based storm characteristics as observed using space- or land-based sensors. The Method of Object-based Diagnostic Evaluation (MODE) Time Domain (MTD) is used to identify and track storm objects in two high-resolution merged datasets: the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) final product V06B and gauge-corrected ground-radar-based Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimations. Characteristics associated with landfalling hurricanes were also examined as a separate category of storm. The results reveal that IMERG and MRMS agree reasonably well across many object-based storm characteristics. However, there are some discrepancies that are statistically significant. MRMS storms are more concentrated, with smaller areas and higher peak intensities, which implies higher flash flood risks associated with the storms. On the other hand, IMERG storms can travel longer distances with a higher volume of precipitation, which implies higher risk of riverine flooding. Agreement between the datasets is higher for faster-moving hurricanes in terms of the averaged intensity. Finally, MRMS indicates a higher average precipitation intensity during the hurricane’s lifetime. However, in non-hurricanes, the opposite result was observed. This is likely related to MRMS having higher resolution; monitoring the hurricanes from many viewing angles, leading to different signal saturation properties compared to IMERG; and/or the dominance of droplet aggregation effects over evaporation effects at lower altitudes.

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Benjamin F. Zaitchik, Jason Evans, and Ronald B. Smith

Abstract

In arid and semiarid parts of the world, evaporation from irrigated fields may significantly influence humidity, near-surface winds, and precipitation. Using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra imagery from summer and autumn 2000 the authors attempt to improve the realism of a regional climate model (the fifth-generation Pennsylvania State University–NCAR Mesoscale Model) with respect to irrigated agriculture. MODIS data were used to estimate spatially distributed vegetation fraction and to identify areas of irrigated land use. Additionally, a novel surface flux routine designed to simulate traditional flood irrigation was implemented. Together these modifications significantly improved model predictions of water flux and the surface energy balance when judged against independent weather station data and known crop requirements. Model estimates of watershed-level water consumption were more than doubled relative to simulations that did not incorporate MODIS data, and there were small but systematic differences in predicted temperature and humidity near the surface. The modified version of the mesoscale model also predicts the existence of heat-driven circulations around large irrigated features, and these circulations are similar in structure and magnitude to those predicted by linear theory. Based on these results, it was found that accurate representation of irrigated agriculture is a prerequisite to any study of the impact of land-use change on climate or on water resources.

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Sanaa Hobeichi, Gab Abramowitz, Steefan Contractor, and Jason Evans

Abstract

Evaluation of global gridded precipitation datasets typically entails using the in situ or satellite-based data used to derive them, so that out-of-sample testing is usually not possible. Here we detail a methodology that incorporates the physical balance constraints of the surface water and energy budgets to evaluate gridded precipitation estimates, providing the capacity for out-of-sample testing. Performance conclusions are determined by the ability of precipitation products to achieve closure of the linked budgets using adjustments that are within their prescribed uncertainty bounds. We evaluate and compare five global gridded precipitation datasets: IMERG, GPCP, GPCC, REGEN, and MERRA-2. At the spatial level, we show that precipitation is best estimated by GPCC over the high latitudes, by GPCP over the tropics, and by REGEN over North Africa and the Middle East. IMERG and REGEN appear best over Australia and South Asia. Furthermore, our results give insight into the adequacy of prescribed uncertainties of these products and shows that MERRA-2, while being less competent than the other four products in estimating precipitation, has the best representation of uncertainties in its precipitation estimates. The spatial extent of our results is not only limited to grid cells with in situ observations. Therefore, the approach enables a robust evaluation of precipitation estimates and goes some way to addressing the challenge of validation over observation scarce regions.

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Benjamin F. Zaitchik, Jason P. Evans, and Ronald B. Smith

Abstract

The authors propose that a heat-driven circulation from the Zagros Plateau has a significant impact on the climate of the Middle East Plain (MEP), especially summertime winds, air temperature, and aridity. This proposal is examined in numerical experiments with a regional climate model. Simulations in which the Zagros Plateau was assigned a highly reflective, “snowlike” albedo neutralized the heat-driven circulation and produced an extra summertime warming of 1°–2°C in the MEP, measured relative to a control simulation and to the records of the NCEP–NCAR reanalysis project. This effect was largest in midsummer, when heating on the plateau was greatest. Additionally, simulations with high albedo on the Zagros showed reduced subsidence and enhanced precipitation in the MEP. These sensitivities are interesting because the Zagros Plateau lies downwind of the MEP. Analysis of model results indicates that the sensitivity of the upwind subsidence region to Zagros albedo can be understood as a linear atmospheric response to plateau heating, communicated upwind by a steady heat-driven circulation that influences the thermodynamic balance of the atmosphere. This regional phenomenon adds to the large-scale subsidence patterns established by the Hadley circulation and the Asian monsoon. Observed patterns of vertical motion in the Middle East, then, are a combined product of Zagros-induced subsidence and hemispheric-scale circulations.

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