36Many regions in Africa and the Middle East are vulnerable to drought and to 37 water and food insecurity, motivating agency efforts such as the U.S. Agency for 38 International Development's (USAID) Famine Early Warning Systems Network (FEWS 39 NET) to provide early warning of drought events in the region. Each year these 40 warnings guide life-saving assistance that reaches millions of people. A new NASA 41 multi-model, remote sensing-based hydrological forecasting and analysis system, 42NHyFAS, has been developed to support such efforts by improving the FEWS NET's 43 current early warning capabilities. NHyFAS derives its skill from two sources: (i) 44 accurate initial conditions, as produced by an offline land modeling system through the 45 application and/or assimilation of various satellite data (precipitation, soil moisture, and 46 terrestrial water storage); and (ii) meteorological forcing data during the forecast period 47 as produced by a state-of-the-art ocean-land-atmosphere forecast system. The land 48 modeling framework used is the Land Information System (LIS), which employs a suite 49 of land surface models, allowing multi-model ensembles and multiple data assimilation 50 strategies to better estimate land surface conditions. An evaluation of NHyFAS shows 51 that its one-to-five month hindcasts successfully capture known historic drought events, 52 and it has improved skill over benchmark type hindcasts. The system also benefits from 53 strong collaboration with end-user partners in Africa and the Middle East, who provide 54 insights on strategies to formulate and communicate early warning indicators to water 55 and food security communities. The additional lead time provided by this system will 56 increase the speed, accuracy and efficacy of humanitarian disaster relief, helping to 57 save lives and livelihoods. 58 Capsule Summary: The new NASA Hydrological Forecast and Analysis System 60 (NHyFAS) provides multi-model seasonal forecasts of hydrological and agricultural 61 drought to end-users, such as the Famine Early Warning Systems Network. 62

The Madden-Julian oscillation (MJO) is the main mode of tropical intraseasonal variations and bridges weather and climate. Because the MJO has a slow eastward propagation and longer time scale relative to synoptic variability, significant interest exists in exploring the predictability of the MJO and its influence on extended-range weather forecasts (i.e., 2-4-week lead times). This study investigates the impact of the MJO on the forecast skill in Northern Hemisphere extratropics during boreal winter. Several 45-day forecasts of geopotential height (500 hPa) from NCEP Climate Forecast System version 2 (CFSv2) reforecasts are used (1 November-31 March 1999. The variability of the MJO expressed as different amplitudes, durations, and recurrence (i.e., primary and successive events) and their influence on forecast skill is analyzed and compared against inactive periods (i.e., null cases). In general, forecast skill during enhanced MJO convection over the western Pacific is systematically higher than in inactive days. When the enhanced MJO convection is over the Maritime Continent, forecasts are lower than in null cases, suggesting potential model deficiencies in accurately forecasting the eastward propagation of the MJO over that region and the associated extratropical response. In contrast, forecasts are more skillful than null cases when the enhanced convection is over the western Pacific and during long, intense, and successive MJO events. These results underscore the importance of the MJO as a potential source of predictability on 2-4-week lead times.

Abstract. The region of southern Africa (SA) has a fragile food economy and is vulnerable to frequent droughts. In 2015–2016, an El Niño-driven drought resulted in major maize production shortfalls, food price increases, and livelihood disruptions that pushed 29 million people into severe food insecurity. Interventions to mitigate food insecurity impacts require early warning of droughts – preferably as early as possible before the harvest season (typically, starting in April) and lean season (typically, starting in November). Hydrologic monitoring and forecasting systems provide a unique opportunity to support early warning efforts, since they can provide regular updates on available rootzone soil moisture (RZSM), a critical variable for crop yield, and provide forecasts of RZSM by combining the estimates of antecedent soil moisture conditions with climate forecasts. For SA, this study documents the predictive capabilities of a recently developed NASA Hydrological Forecasting and Analysis System (NHyFAS). The NHyFAS system's ability to forecast and monitor the 2015/2016 drought event is evaluated. The system's capacity to explain interannual variations in regional crop yield and identify below-normal crop yield events is also evaluated. Results show that the NHyFAS products would have identified the regional severe drought event, which peaked during December–February of 2015/2016, at least as early as 1 November 2015. Next, it is shown that February RZSM forecasts produced as early as 1 November (4–5 months before the start of harvest and about a year before the start of the next lean season) correlate fairly well with regional crop yields (r = 0.49). The February RZSM monitoring product, available in early March, correlates with the regional crop yield with higher skill (r = 0.79). It is also found that when the February RZSM forecast produced on November 1 is indicated to be in the lowest tercile, the detrended regional crop yield is below normal about two-thirds (significance level ~ 86 %) of the time. Furthermore, when the February RZSM monitoring product (available in early March) indicates a lowest tercile value, the crop yield is always below normal, at least over the sample years considered. These results indicate that the NHyFAS products can effectively support food insecurity early warning in the SA region.

The space-time structure of the leading monsoon intraseasonal oscillation (MISO) in three-dimensional diabatic heating is studied. Using the ERA-Interim data of the European Centre for Medium-Range Weather Forecasts, the diabatic heating data were constructed by the residual method of the thermodynamic equation. The MISO was extracted by applying multichannel singular spectrum analysis on the daily anomalies of threedimensional diabatic heating over the South Asian monsoon region for the period 1979-2011.The diabatic heating MISO has a period of 45 days, and exhibits eastward propagation in the equatorial Indian and Pacific Oceans and northward propagation over the entire monsoon region. The horizontal structure shows a long tilted band of heating anomalies propagating northeastward. The period, horizontal pattern, and propagation properties of the diabatic heating MISO are similar to those found in precipitation, outgoing longwave radiation, and circulation in earlier studies. The vertical structure of the diabatic heating MISO indicates deep columns, with maximum values at about 450 hPa, propagating northeastward. The vertical structure of the heating anomalies has good correspondence with that of the moisture anomalies but with a phase difference. The moisture anomalies lead the heating anomalies and may provide a preconditioning process for the propagation mechanism. The temperature anomalies also show oscillatory behavior corresponding to the diabatic heating MISO but the phase difference between the two varies from region to region.

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Abstract. The Middle East and North Africa (MENA) region has experienced more frequent and severe drought events in recent decades, leading to increasingly pressing concerns over already strained food and water security. An effective drought monitoring and early warning system is thus critical to support risk mitigation and management by countries in the region. Here we investigate the potential for assimilation of leaf area index (LAI) and soil moisture observations to improve representation of the overall hydrological and carbon cycles and drought by an advanced land surface model. The results reveal that assimilating soil moisture does not meaningfully improve model representation of the hydrological and biospheric processes for this region, but rather it degrades simulation of interannual variation of evapotranspiration (ET) and carbon fluxes, mainly due to model weaknesses in representing dynamic phenology. However, assimilating LAI leads to greater improvement, especially for transpiration and carbon fluxes, by constraining the timing of simulated vegetation growth response to evolving climate conditions. LAI assimilation also helps to correct for the erroneous interaction between the dynamic phenology and irrigation during summertime, effectively reducing a large positive bias in ET and carbon fluxes. Independently assimilating LAI or soil moisture alters the categorization of drought, with the differences being greater for more severe drought categories. We highlight the vegetation representation in response to changing land use and hydroclimate as one of the key processes to be captured for building a successful drought early warning system for the MENA region.

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Abstract. The Middle East and North Africa (MENA) region has experienced more frequent and severe drought events in recent decades, leading to increasingly pressing concerns over already strained food and water security. An effective drought monitoring and early warning system is thus critical to support risk mitigation and management by countries in the region. Here we investigate the potential for assimilation of leaf area index (LAI) and soil moisture observations to improve the representation of the overall hydrological and carbon cycles and drought by an advanced land surface model. The results reveal that assimilating soil moisture does not meaningfully improve model representation of the hydrological and biospheric processes for this region, but instead it degrades the simulation of the interannual variation in evapotranspiration (ET) and carbon fluxes, mainly due to model weaknesses in representing prognostic phenology. However, assimilating LAI leads to greater improvement, especially for transpiration and carbon fluxes, by constraining the timing of simulated vegetation growth response to evolving climate conditions. LAI assimilation also helps to correct for the erroneous interaction between the prognostic phenology and irrigation during summertime, effectively reducing a large positive bias in ET and carbon fluxes. Independently assimilating LAI or soil moisture alters the categorization of drought, with the differences being greater for more severe drought categories. We highlight the vegetation representation in response to changing land use and hydroclimate as one of the key processes to be captured for building a successful drought early warning system for the MENA region.