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Yue Li, James T. Randerson, Natalie M. Mahowald, and Peter J. Lawrence

Abstract

Phosphorus contained in atmospheric mineral dust aerosol originating from Africa fertilizes tropical forests in Amazonia. However, the mechanisms influencing this nutrient transport pathway remain poorly understood. Here we use the Community Earth System Model to investigate how large-scale deforestation affects mineral dust aerosol transport and deposition in the tropics. We find that the surface biophysical changes that accompany deforestation produce a warmer, drier, and windier surface environment that perturbs atmospheric circulation and enhances long-range dust transport from North Africa to the Amazon. Tropics-wide deforestation weakens the Hadley circulation, which then leads to a northward expansion of the Hadley cell and increases surface air pressure over the Sahara Desert. The high pressure anomaly over the Sahara, in turn, increases northeasterly winds across North Africa and the tropical North Atlantic Ocean, which subsequently increases dust transport to the South American continent. We estimate that the annual atmospheric phosphorus deposition from dust significantly increases by 27% (P < 0.01) in the Amazon under a scenario of complete deforestation. These interactions exemplify how land surface changes can modify tropical nutrient cycling, which, in turn, may have consequences for long-term changes in tropical ecosystem productivity and biodiversity.

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Samantha Ferrett, Thomas H. A. Frame, John Methven, Christopher E. Holloway, Stuart Webster, Thorwald H. M. Stein, and Carlo Cafaro

Abstract

Forecasting rainfall in the tropics is a major challenge for numerical weather prediction. Convection-permitting (CP) models are intended to enable forecasts of high-impact weather events. Development and operation of these models in the tropics has only just been realized. This study describes and evaluates a suite of recently developed Met Office Unified Model CP ensemble forecasts over three domains in Southeast Asia, covering Malaysia, Indonesia, and the Philippines. The fractions skill score is used to assess the spatial scale dependence of skill in forecasts of precipitation during October 2018–March 2019. CP forecasts are skillful for 3-h precipitation accumulations at spatial scales greater than 200 km in all domains during the first day of forecasts. Skill decreases with lead time but varies depending on time of day over Malaysia and Indonesia, due to the importance of the diurnal cycle in driving rainfall in those regions. Skill is largest during daytime when precipitation is over land and is constrained by orography. Comparison of CP ensembles using 2.2-, 4.5-, and 8.8-km grid spacing and an 8.8-km ensemble with parameterized convection reveals that varying resolution has much less effect on ensemble skill and spread than the representation of convection. The parameterized ensemble is less skillful than CP ensembles over Malaysia and Indonesia and more skillful over the Philippines; however, the parameterized ensemble has large drops in skill and spread related to deficiencies in its diurnal cycle representation. All ensembles are underspread indicating that future model development should focus on this issue.

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Amit Bhardwaj, Vasubandhu Misra, Ben Kirtman, Tirusew Asefa, Carolina Maran, Kevin Morris, Ed Carter, Christopher Martinez, and Daniel Roberts

Abstract

We present here the analysis of 20 years of high-resolution experimental winter seasonal climate reforecasts for Florida (CLIFF). These winter seasonal reforecasts were dynamically downscaled by a regional atmospheric model at 10-km grid spacing from a global model run at T62 spectral resolution (~210-km grid spacing at the equator) forced with sea surface temperatures (SST) obtained from one of the global models in the North American Multimodel Ensemble (NMME). CLIFF was designed in consultation with water managers (in utilities and public water supply) in Florida targeting its five water management districts, including two smaller watersheds of two specific stakeholders in central Florida that manage the public water supply. This enterprise was undertaken in an attempt to meet the climate forecast needs of water management in Florida. CLIFF has 30 ensemble members per season generated by changes to the physics and the lateral boundary conditions of the regional atmospheric model. Both deterministic and probabilistic skill measures of the seasonal precipitation at the zero-month lead for November–December–January (NDJ) and one-month lead for December–January–February (DJF) show that CLIFF has higher seasonal prediction skill than persistence. The results of the seasonal prediction skill of land surface temperature are more sobering than precipitation, although, in many instances, it is still better than the persistence skill.

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Aaron J. Hill, Christopher C. Weiss, and David C. Dowell

Abstract

Ensemble forecasts are generated with and without the assimilation of near-surface observations from a portable, mesoscale network of StickNet platforms during the Verification of the Origins of Rotation in Tornadoes Experiment–Southeast (VORTEX-SE). Four VORTEX-SE intensive observing periods are selected to evaluate the impact of StickNet observations on forecasts and predictability of deep convection within the Southeast United States. StickNet observations are assimilated with an experimental version of the High-Resolution Rapid Refresh Ensemble (HRRRE) in one experiment, and withheld in a control forecast experiment. Overall, StickNet observations are found to effectively reduce mesoscale analysis and forecast errors of temperature and dewpoint. Differences in ensemble analyses between the two parallel experiments are maximized near the StickNet array and then either propagate away with the mean low-level flow through the forecast period or remain quasi-stationary, reducing local analysis biases. Forecast errors of temperature and dewpoint exhibit periods of improvement and degradation relative to the control forecast, and error increases are largely driven on the storm scale. Convection predictability, measured through subjective evaluation and objective verification of forecast updraft helicity, is driven more by when forecasts are initialized (i.e., more data assimilation cycles with conventional observations) rather than the inclusion of StickNet observations in data assimilation. It is hypothesized that the full impact of assimilating these data is not realized in part due to poor sampling of forecast sensitive regions by the StickNet platforms, as identified through ensemble sensitivity analysis.

Open access
Samson Hagos, L. Ruby Leung, Oluwayemi Garuba, and Christina M. Patricola

Abstract

The frequency of North Pacific atmospheric rivers (ARs) affects water supply and flood risk over western North America. Thus, understanding factors that affect the variability of landfalling AR frequency is of scientific and societal importance. This study aims at identifying the sources of the moisture for North Pacific ARs and assessing how different modes of variability modulate these sources. To this end, the sources and variability of the background divergent component of the integrated moisture flux (DIVT) in ARs are identified using MERRA reanalysis. It is shown that in the boreal winter, this background DIVT in ARs is related to the outflow from the subsidence over the subtropics that transports moisture northward, while in summer it is related to the Asian monsoon and it transports moisture northwestward. This leads to a seasonal northwest–southeast movement of the AR frequency climatology. At the intraseasonal scale, propagation of the Madden–Julian oscillation introduces an anticlockwise rotation of the background DIVT, with northward transport in phases 1 and 2, westward in 3 and 4, southward in 5 and 6, and eastward in 7 and 8, making landfall over the west coast of North America most likely during the last two phases. Similarly, El Niño–Southern Oscillation variability also affects the frequency of ARs through modulation of the westerly background DIVT, favoring landfall over the U.S. West Coast during strong El Niño phases. It is shown that in general the likelihood of AR landfall over the western United States is correlated with the zonal background DIVT over northeastern Pacific.

Open access
Ana C. T. Sena and Gudrun Magnusdottir

Abstract

The influence of each phase of the Indian Ocean dipole (IOD) on the large-scale circulation in South America is investigated using rainfall observations, fully coupled, large-ensemble, historical simulations (LENS), and forced experiments using the coupled model’s atmospheric component. IOD events often occur when El Niño–Southern Oscillation (ENSO), the largest source of interannual variability of precipitation in South America, is active. To distinguish from effects of ENSO, only cases during neutral ENSO conditions are analyzed in LENS and observations. During the positive IOD polarity, a perturbation in the local Walker circulation leads to increased convection over equatorial South America, resulting in wet anomalies in the Amazon basin. This signal is the opposite of what is expected during El Niño events. Tropical convection anomalies in the Indian Ocean also force an extratropical Rossby wave train that reaches subtropical South America. During positive IOD, the moisture flux from the Amazon to central and southeastern Brazil weakens, resulting in a drying of the area associated with the South Atlantic convergence zone. Meanwhile, the South Atlantic subtropical high strengthens, contributing to a drying in southeastern Brazil. During negative IOD, the induced wave train from the Indian Ocean leads to increased moisture transport to the La Plata basin, leading to wet anomalies in the region.

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Soichiro Hirano, Masashi Kohma, and Kaoru Sato

Abstract

The relation between interannual variability of stratospheric final warming (SFW) and tropospheric circulation in the Southern Hemisphere (SH) is explored using reanalysis data and a linear barotropic model. The analysis is focused on quasi-stationary waves with zonal wavenumber 1 (s = 1 QSWs; s is zonal wavenumber), which are the dominant component of the SH extratropical planetary waves. First, interannual variability of SFW is investigated in terms of amplitudes of stratospheric and tropospheric s = 1 QSWs, and wave transmission properties of the mean flow from the late austral winter to spring. Upward Eliassen–Palm flux due to s = 1 QSWs is larger from the stratosphere down to the middle troposphere in early-SFW years than late-SFW years. More favorable conditions for propagation of s = 1 stationary waves into the stratosphere are identified in early-SFW years. These results indicate that the amplification of tropospheric s = 1 QSWs and the favorable conditions for their propagation into the stratosphere lead to the amplification of stratospheric s = 1 QSWs and hence earlier SFWs. Next, numerical calculations using a linear barotropic model are performed to explore how tropospheric s = 1 QSWs at high latitudes amplifies in early-SFW years. By using tropical Rossby wave source and horizontal winds in the reanalysis data as a source and background field, respectively, differences in s = 1 steady responses between early- and late-SFWs are examined at high latitudes. It is suggested that the larger amplitudes of tropospheric s = 1 QSWs in early-SFW years are attributed to differences in wave propagation characteristics associated with structure of the midlatitude jets in austral spring.

Open access
Cynthia Garcia-Eidell, Josefino C. Comiso, Max Berkelhammer, and Larry Stock

Abstract

Satellite data can now provide a coherent picture of sea surface salinity (SSS), chlorophyll-α concentration (Chlα), sea surface temperature (SST), and sea ice cover across the Southern Ocean. The availability of these data at the basin scale enables novel insight into the physical and biological processes in an area that has historically been difficult to gather in situ data from. The analysis shows large regional and interannual variability of these parameters but also strong coherence across the Southern Ocean. The covariability of the parameters near the marginal ice zone shows a generally negative relationship between SSS and Chlα (r = −0.87). This may in part be attributed to the large seasonality of the variables, but analysis of data within the spring period (from November to December) shows similarly high correlation (r = −0.81). This is the first time that a large-scale robust connection between low salinity and high phytoplankton concentration during ice melt period has been quantified. Chlorophyll-α concentration is also well correlated with SST (r = 0.79) providing a potential indicator of the strength of the temperature limitation on primary productivity in the region. The observed correlation also varied regionally due to differences in ice melt patterns during spring and summer. Overall, this study provides new insights into the physical characteristics of the Southern Ocean as observed from space. In a continually warming and freshening Southern Ocean, the relationships observed here provide a key data source for testing ocean biogeochemical models and assessing the effect of sea ice–ocean processes on primary production.

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Yaru Guo, Yuanlong Li, Fan Wang, and Yuntao Wei

Abstract

Ningaloo Niño—the interannually occurring warming episode in the southeast Indian Ocean (SEIO)—has strong signatures in ocean temperature and circulation and exerts profound impacts on regional climate and marine biosystems. Analysis of observational data and eddy-resolving regional ocean model simulations reveals that the Ningaloo Niño/Niña can also induce pronounced variability in ocean salinity, causing large-scale sea surface salinity (SSS) freshening of 0.15–0.20 psu in the SEIO during its warm phase. Model experiments are performed to understand the underlying processes. This SSS freshening is mutually caused by the increased local precipitation (~68%) and enhanced freshwater transport of the Indonesian Throughflow (ITF; ~28%) during Ningaloo Niño events. The effects of other processes, such as local winds and evaporation, are secondary (~18%). The ITF enhances the southward freshwater advection near the eastern boundary, which is critical in causing the strong freshening (>0.20 psu) near the Western Australian coast. Owing to the strong modulation effect of the ITF, SSS near the coast bears a higher correlation with El Niño–Southern Oscillation (0.57, 0.77, and 0.70 with the Niño-3, Niño-4, and Niño-3.4 indices, respectively) than sea surface temperature (−0.27, −0.42, and −0.35) during 1993–2016. Yet, an idealized model experiment with artificial damping for salinity anomaly indicates that ocean salinity has limited impact on ocean near-surface stratification and thus minimal feedback effect on the warming of Ningaloo Niño.

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Rachel Kim, L. Bruno Tremblay, Charles Brunette, and Robert Newton

Abstract

Thinning sea ice cover in the Arctic is associated with larger interannual variability in the minimum sea ice extent (SIE). The current generation of forced or fully coupled models, however, has difficulty predicting SIE anomalies from the long-term trend, highlighting the need to better identify the mechanisms involved in the seasonal evolution of sea ice cover. One such mechanism is coastal divergence (CD), a proxy for ice thickness anomalies based on late winter ice motion, quantified using Lagrangian ice tracking. CD gains predictive skill through the positive feedback of surface albedo anomalies, mirrored in reflected solar radiation (RSR), during melt season. Exploring the dynamic and thermodynamic contributions to minimum SIE predictability, RSR, initial SIE (iSIE), and CD are compared as predictors using a regional seasonal sea ice forecast model for 1 July, 1 June, and 1 May forecast dates for all Arctic peripheral seas. The predictive skill of June RSR anomalies mainly originates from open water fraction at the surface; that is, June iSIE and June RSR have equal predictive skill for most seas. The finding is supported by the surprising positive correlation found between June melt pond fraction (MPF) and June RSR in all peripheral seas: MPF anomalies indicate the presence of ice or open water, which is key to creating minimum SIE anomalies. This contradicts models that show correlation between melt onset, MPF, and the minimum SIE. A hindcast model shows that for a 1 May forecast, CD anomalies have better predictive skill than RSR anomalies for most peripheral seas.

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