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Omar V. Müller, Miguel A. Lovino, and Ernesto H. Berbery

Abstract

Weather forecasting and monitoring systems based on regional models are becoming increasingly relevant for decision support in agriculture and water management. This work evaluates the predictive and monitoring capabilities of a system based on WRF Model simulations at 15-km grid spacing over the La Plata basin (LPB) in southern South America, where agriculture and water resources are essential. The model’s skill up to a lead time of 7 days is evaluated with daily precipitation and 2-m temperature in situ observations for the 2-yr period from 1 August 2012 to 31 July 2014. Results show high prediction performance with 7-day lead time throughout the domain and particularly over LPB, where about 70% of rain and no-rain days are correctly predicted. Also, the probability of detection of rain days is above 80% in humid regions. Temperature observations and forecasts are highly correlated (r > 0.80) while mean absolute errors, even at the maximum lead time, remain below 2.7°C for minimum and mean temperatures and below 3.7°C for maximum temperatures. The usefulness of WRF products for hydroclimate monitoring was tested for an unprecedented drought in southern Brazil and for a slightly above normal precipitation season in northeastern Argentina. In both cases the model products reproduce the observed precipitation conditions with consistent impacts on soil moisture, evapotranspiration, and runoff. This evaluation validates the model’s usefulness for forecasting weather up to 1 week in advance and for monitoring climate conditions in real time. The scores suggest that the forecast lead time can be extended into a second week, while bias correction methods can reduce some of the systematic errors.

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Omar V. Müller, Ernesto Hugo Berbery, Domingo Alcaraz-Segura, and Michael B. Ek

An incorrect version of Fig. 13a was published in Müller et al. (2014). Figure 13a on page 6771 did not show the gray band representing the eastern region area-averaged 2-m temperature for CPC and OBS (Climate Prediction Center and Observations). The correct figure is provided here. We regret any inconvenience.

Area-averaged 2-m temperature for CPC and OBS (their slight differences are shaded) CTL and EFT over two regions denoted to the left: (a) eastern region and (b) central region. Only data over land were considered.

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Omar V. Müller, Ernesto Hugo Berbery, Domingo Alcaraz-Segura, and Michael B. Ek

Abstract

This work discusses the land surface–atmosphere interactions during the severe drought of 2008 in southern South America, which was among the most severe in the last 50 years in terms of both intensity and extent. Once precipitation returned to normal values, it took about two months for the soil moisture content and vegetation to recover. The land surface effects were examined by contrasting long-term simulations using a consistent set of satellite-derived annually varying land surface biophysical properties against simulations using the conventional land-cover types in the Weather Research and Forecasting Model–Noah land surface model (WRF–Noah). The new land-cover dataset is based on ecosystem functional properties that capture changes in vegetation status due to climate anomalies and land-use changes.

The results show that the use of realistic information of vegetation states enhances the model performance, reducing the precipitation biases over the drought region and over areas of excessive precipitation. The precipitation bias reductions are attributed to the corresponding changes in greenness fraction, leaf area index, stomatal resistance, and surface roughness. The temperature simulation shows a generalized increase, which is attributable to a lower vegetation greenness and a doubling of the stomatal resistance that reduces the evapotranspiration rate. The increase of temperature has a beneficial effect toward the eastern part of the domain with a notable reduction of the bias, but not over the central region where the bias is increased. The overall results suggest that an improved representation of the surface processes may contribute to improving the predictive skill of the model system.

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Omar V. Müller, Pier Luigi Vidale, Benoît Vannière, Reinhard Schiemann, and Patrick C. McGuire

Abstract

Previous studies showed that high-resolution GCMs overestimate land precipitation when compared against observation-based data. Particularly, high-resolution HadGEM3-GC3.1 shows a significant precipitation increase in mountainous regions, where the scarcity of gauge stations increases the uncertainty of gridded observations and reanalyses. This work evaluates such precipitation uncertainties indirectly through the assessment of river discharge, considering that an increase of ~10% in land precipitation produces ~28% more runoff when the resolution is enhanced from 1° to 0.25°, and ~50% of the global runoff is produced in 27% of global land dominated by mountains. We diagnosed the river flow by routing the runoff generated by HadGEM3-GC3.1 low- and high-resolution simulations. The river flow is evaluated using a set of 344 monitored catchments distributed around the world. We also infer the global discharge by constraining the simulations with observations following a novel approach that implies bias correction in monitored rivers with two methods, and extension of the correction to the river mouth, and along the coast. Our global discharge estimate is 47.4 ± 1.6 × 103 km3 yr−1, which is closer to the original high-resolution estimate (50.5 × 103 km3 yr−1) than to the low-resolution (39.6 × 103 km3 yr−1). The assessment suggests that high-resolution simulations perform better in mountainous regions, either because the better-defined orography favors the placement of precipitation in the correct catchment, leading to a more accurate distribution of runoff, or the orographic precipitation increases, reducing the dry runoff bias of coarse-resolution simulations. However, high-resolution slightly increases wet biases in catchments dominated by flat terrain. The improvement of model parameterizations and tuning may reduce the remaining errors in high-resolution simulations.

Open access
Omar V. Müller, Pier Luigi Vidale, Benoît Vannière, Reinhard Schiemann, Retish Senan, Reindert J. Haarsma, and Johann H. Jungclaus

Abstract

Land–atmosphere interactions are often interpreted as local effects, whereby the soil state drives local atmospheric conditions and feedbacks originate. However, nonlocal mechanisms can significantly modulate land–atmosphere exchanges and coupling. We make use of GCMs at different resolutions (low ~1° and high ~0.25°) to separate the two contributions to coupling: better represented local processes versus the influence of improved large-scale circulation. We use a two-legged metric, complemented by a process-based assessment of four CMIP6 GCMs. Our results show that weakening, strengthening, and relocation of coupling hot spots occur at high resolution globally. The northward expansion of the Sahel hot spot, driven by nonlocal mechanisms, is the most notable change. The African easterly jet’s horizontal wind shear is enhanced in JJA due to better resolved orography at high resolution. This effect, combined with enhanced easterly moisture flux, favors the development of African easterly waves over the Sahel. More precipitation and soil moisture recharge produce strengthening of the coupling, where evapotranspiration remains controlled by soil moisture, and weakening where evapotranspiration depends on atmospheric demand. In SON, the atmospheric influence is weaker, but soil memory helps to maintain the coupling between soil moisture and evapotranspiration and the relocation of the hot spot at high resolution. The multimodel agreement provides robust evidence that atmospheric dynamics determines the onset of land–atmosphere interactions, while the soil state modulates their duration. Comparison of precipitation, soil moisture, and evapotranspiration against satellite data reveals that the enhanced moistening at high resolution significantly reduces model biases, supporting the realism of the hot-spot relocation.

Open access
Siegfried D. Schubert, Ronald E. Stewart, Hailan Wang, Mathew Barlow, Ernesto H. Berbery, Wenju Cai, Martin P. Hoerling, Krishna K. Kanikicharla, Randal D. Koster, Bradfield Lyon, Annarita Mariotti, Carlos R. Mechoso, Omar V. Müller, Belen Rodriguez-Fonseca, Richard Seager, Sonia I. Seneviratne, Lixia Zhang, and Tianjun Zhou

Abstract

Drought affects virtually every region of the world, and potential shifts in its character in a changing climate are a major concern. This article presents a synthesis of current understanding of meteorological drought, with a focus on the large-scale controls on precipitation afforded by sea surface temperature (SST) anomalies, land surface feedbacks, and radiative forcings. The synthesis is primarily based on regionally focused articles submitted to the Global Drought Information System (GDIS) collection together with new results from a suite of atmospheric general circulation model experiments intended to integrate those studies into a coherent view of drought worldwide. On interannual time scales, the preeminence of ENSO as a driver of meteorological drought throughout much of the Americas, eastern Asia, Australia, and the Maritime Continent is now well established, whereas in other regions (e.g., Europe, Africa, and India), the response to ENSO is more ephemeral or nonexistent. Northern Eurasia, central Europe, and central and eastern Canada stand out as regions with few SST-forced impacts on precipitation on interannual time scales. Decadal changes in SST appear to be a major factor in the occurrence of long-term drought, as highlighted by apparent impacts on precipitation of the late 1990s “climate shifts” in the Pacific and Atlantic SST. Key remaining research challenges include (i) better quantification of unforced and forced atmospheric variability as well as land–atmosphere feedbacks, (ii) better understanding of the physical basis for the leading modes of climate variability and their predictability, and (iii) quantification of the relative contributions of internal decadal SST variability and forced climate change to long-term drought.

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