Turbulent fluxes of heat and moisture across the atmosphere–ocean interface are fundamental components of the earth’s energy and water balance. Characterizing both the spatiotemporal variability and the fidelity of these exchanges of heat and moisture is critical to understanding the global water and energy cycle variations, quantifying atmosphere–ocean feedbacks, and improving model predictability. This study examines the veracity of the recently completed NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) product in terms of its turbulent surface fluxes. This assessment employs a large dataset of directly measured turbulent fluxes as well as other turbulent surface flux datasets. The spatial and temporal variability of the surface fluxes are examined in terms of their annual-mean climatologies, their seasonal covariability of near-surface bulk parameters, and their representation of extremes. The impact of data assimilation on the near-surface parameters is assessed through evaluation of the incremental analysis update tendencies. It is found that MERRA turbulent surface fluxes are relatively accurate for typical conditions but have systematically weak vertical gradients in moisture and temperature and a weaker covariability between the near-surface gradients and wind speed than found in observations. This results in an underestimate of the surface latent and sensible heat fluxes over the western boundary current and storm-track regions. The assimilation of observations generally acts to bring MERRA closer to observational products by increasing moisture and temperature near the surface and decreasing the near-surface wind speeds.

The objective of this study is to evaluate the ability of soil physical characteristics (i.e., texture and moisture conditions) to better understand the breeding conditions of desert locust (DL). Though soil moisture and texture are well-known and necessary environmental conditions for DL breeding, in this study, we highlight the ability of model-derived soil moisture estimates to contribute towards broader desert locust monitoring activities. We focus on the recent DL upsurge in East Africa from October 2019 though June 2020, utilizing known locust observations from the United Nations Food and Agriculture Organization (FAO). We compare this information to results from the current literature and combine the two datasets to create “optimal thresholds” of breeding conditions. When considering the most optimal conditions (all thresholds met), the soil texture combined with modeled soil moisture content predicted the estimated DL egg-laying period 62.5% of the time. Accounting for the data errors and uncertainties, a 3 × 3 pixel buffer increased this to 85.2%. By including soil moisture, the areas of optimal egg laying conditions decreased from 33% to less than 20% on average.

Ocean evaporative fluxes are a critical component of the Earth's energy and water cycle, but their estimation remain uncertain. Near‐surface humidity is a required input to bulk flux algorithms that relate mean surface values to the turbulent fluxes. Several satellite‐derived turbulent flux products have been developed over the last decade that utilize passive microwave imager observations to estimate the surface humidity. It is known however that these estimates tend to diverge from one another and from in situ observations. Analysis of current state‐of‐the‐art satellite estimates provided herein reveals that regional‐scale biases in these products remain significant. Investigations reveal a link between the spatial coherency of the observed biases to atmospheric dynamical controls of water vapor vertical stratification, cloud liquid water, and sea surface temperature. This information is used to develop a simple state‐dependent bias correction that results in more consistent ocean surface humidity estimates. A principal conclusion is that further improvements to ocean near‐surface humidity estimation using microwave radiometers requires incorporation of prior information on water vapor stratification and sea surface temperature.

The accurate estimation of near‐surface marine‐specific humidity is necessary for climate and air–sea interaction applications. Available estimates of monthly mean‐specific humidity spanning the past 50‐years are based on a variety of sources including in situ observations, atmospheric reanalyses and datasets that blend many different data sources. Eight specific humidity datasets are compared and little consensus emerges as to mean values, regional variations and changes over time. For large area averages the datasets do show consistency in their interannual variations and, in the Extratropics, in their seasonal cycles. Adjustments applied to in situ observations from ships are shown to be smaller than differences among the datasets and in well‐sampled regions and periods the in situ data are able to highlight biases in the reanalysis‐based specific humidity estimates. Near surface‐specific humidity estimates from two recent atmospheric reanalysis projects show markedly different responses in Tropical‐specific humidity to the assimilation of satellite radiance measurements that became available in 1999. There is less confidence in reanalysis‐based estimates of specific humidity over the ocean than over land. However the in situ‐based humidity analyses have suffered in recent years with a reduction in observation numbers and lack of information on observation methods and heights. Consequently near‐surface‐specific humidity remains relatively poorly known over the oceans.

Kenya is highly dependent on precipitation for both food and water security. Farmers and pastoralists rely on rain to provide water for crops and vegetation to feed herds. As such, precipitation forecasts can be useful tools to inform decision makers and potentially allow the preparation for such events as drought. This study assessed the predictability of a seasonal forecast (CFSv2) and a short-term precipitation forecast (CHIRPS-GEFS) over Kenya. The short-term forecast was assessed on its ability to predict the onset date of the rainy season, and the skill of the seasonal forecast in predicting abnormal precipitation patterns. CHIRPS-GEFS provided a useful starting point to estimate the onset date, but during the long rains in the southwest, where agriculture is concentrated, differences between the predicted and actual onset dates were large (over 20 days). Assessments for CFSv2 generally displayed lower forecast skill over highlands and coastal regions at a seasonal scale. The CFSv2 forecast skill varied widely over individual months and lead times, but over whole rainy seasons, CFSv2 was more skillful than a random forecast at all lead times in the major agricultural areas of Kenya. This research fills a critical research and application gap in understanding the forecast precipitation skill for onset and sub-seasonal prediction.