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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 in turn, 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 America 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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Will Boyles and Matthias Katzfuss

Abstract

The ensemble Kalman filter (EnKF) is a popular technique for data assimilation in high-dimensional nonlinear state-space models. The EnKF represents distributions of interest by an ensemble, which is a form of dimension reduction that enables straightforward forecasting even for complicated and expensive evolution operators. However, the EnKF update step involves estimation of the forecast covariance matrix based on the (often small) ensemble, which requires regularization. Many existing regularization techniques rely on spatial localization, which may ignore long-range dependence. Instead, our proposed approach assumes a sparse Cholesky factor of the inverse covariance matrix, and the nonzero Cholesky entries are further regularized. The resulting method is highly flexible and computationally scalable. In our numerical experiments, our approach was more accurate and less sensitive to misspecification of tuning parameters than tapering-based localization.

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Xia Pengfei, Ye Shirong, Xu Caijun, and Jiang Weiping

Abstract

Tropospheric hydrostatic delay is one of the major source of errors in Global Navigation Satellite System (GNSS) navigation and positioning, and an important parameter in GNSS meteorology. This work first proposes a new method of computing zenith hydrostatic delay (ZHD) based on precipitable water vapor (PWV), using radiosonde data. Next, using these calculations as a reference, the performance of three empirical ZHD models and three ZHD integral models in China is assessed using benchmark values obtained from 8 years (2010-2017) of radiosonde data recorded at 75 stations across China. Finally, we provide a new revised ZHD model that can be applied to China and validate its performance using radiosonde data collected in China in 2018. The statistical results indicate that the ZHD can be estimated by this new model with an accuracy of several millimeters. Due to its performance and simplicity, this new model is shown to be the optimal ZHD model for use in China.

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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 10km grid spacing from a global model run at T62 spectral resolution (~210km 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 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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David W. Pierce, Lu Su, Daniel R. Cayan, Mark D. Risser, Ben Livneh, and Dennis P. Lettenmaier

Abstract

Extreme daily precipitation contributes to flooding that can cause significant economic damages, and so is important to properly capture in gridded meteorological data sets. This work examines precipitation extremes, the mean precipitation on wet days, and fraction of wet days in two widely used gridded data sets over the conterminous U.S. (CONUS). Compared to the underlying station observations, the gridded data show a 27% reduction in annual 1-day maximum precipitation, 25% increase in wet day fraction, 1.5 to 2.5 day increase in mean wet spell length, 30% low bias in 20-year return values of daily precipitation, and 25% decrease in mean precipitation on wet days. It is shown these changes arise primarily from the time-adjustment applied to put the precipitation gauge observations into a uniform time frame, with the gridding process playing a lesser role. A new daily precipitation data set is developed that omits the time-adjustment (as well as extending the gridded data by 7 years) and is shown to perform significantly better in reproducing extreme precipitation metrics. When the new data set is used to force a land surface model, annually averaged 1-day maximum runoff increases 38% compared to the original data, annual mean runoff increases 17%, evapotranspiration drops 2.3%, and fewer wet days leads to a 3.3% increase in estimated solar insolation. These changes are large enough to affect portrayals of flood risk and water balance components important for ecological and climate-change applications across the CONUS.

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Michael Diaz and William R. Boos

Abstract

This study explores the effect of surface sensible and latent heat fluxes on monsoon depressions using a series of idealized convection-permitting simulations. Each experiment is initialized with a small amplitude wave that is allowed to grow within an environment representative of the South Asian monsoon. Comparing experiments with and without interactive surface heat fluxes, it is found that these fluxes enhance the growth of the simulated vortices. Without interactive surface fluxes, the strengthening period is short and the vortices fail to reach intensities characteristic of stronger monsoon depressions. Using a large set of experiments in which the vertical and meridional shear are systematically varied, it is found that surface heat fluxes enhance intensity the most when the upper-level shear is weak, the lower-level shear and associated moist static energy (MSE) gradient are sufficiently steep, and the lower-level meridional shear is strong. These experiments reveal two different regimes of convection-coupled monsoon depression growth: one in which convection is driven by MSE advection and one in which it is driven by surface heat fluxes and quasi-geostrophic forcing for ascent. Both regimes require sufficiently strong meridional shear to achieve initial growth by barotropic instability.

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Gerald V. Frost, Uma S. Bhatt, Matthew J. Macander, Amy S. Hendricks, and M. Torre Jorgenson

Abstract

Alaska’s Yukon-Kuskokwim Delta (YKD) is among the Arctic’s warmest, most biologically productive regions, but regional decline of the Normalized Difference Vegetation Index (NDVI) has been a striking feature of spaceborne Advanced High Resolution Radiometer (AVHRR) observations since 1982. This contrast with “greening” prevalent elsewhere in the Low Arctic raises questions concerning climatic and biophysical drivers of tundra productivity along maritime-continental gradients. We compared NDVI time-series from AVHRR, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Landsat for 2000–2019, and identified trend drivers with reference to sea-ice and climate datasets, ecosystem and disturbance mapping, field measurements of vegetation, and knowledge exchange with YKD elders. All time-series showed increasing maximum NDVI; however, while MODIS and Landsat trends were very similar, AVHRR-observed trends were weaker and had dissimilar spatial patterns. The AVHRR and MODIS records for time-integrated NDVI were dramatically different; AVHRR indicated weak declines, whereas MODIS indicated strong increases throughout the YKD. Disagreement largely arose from observations during shoulder seasons, when there is partial snow cover and very high cloud frequency. Nonetheless, both records shared strong correlations with spring sea-ice extent and summer warmth. Multiple lines of evidence indicate that despite frequent disturbances and high interannual variability in spring sea-ice and summer warmth, tundra productivity is increasing on the YKD. Although climatic drivers of tundra productivity were similar to more continental parts of the Arctic, our intercomparison highlights sources of uncertainty in maritime areas like the YKD that currently, or soon will challenge historical concepts of “what is Arctic.”

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Guo-Yuan Lien, Chung-Han Lin, Zih-Mao Huang, Wen-Hsin Teng, Jen-Her Chen, Ching-Chieh Lin, Hsu-Hui Ho, Jyun-Ying Huang, Jing-Shan Hong, Chia-Ping Cheng, and Ching-Yuang Huang

Abstract

The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched on June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semi-operational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the Ensemble Forecast Sensitivity to Observation Impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.

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Tobias Goecke and Ekaterina Machulskaya

Abstract

We present a detailed evaluation of the turbulence forecast product EDP (Eddy Dissipation Parameter) used at the Deutscher Wetterdienst (DWD). It is based on the turbulence parameterization scheme TURBDIFF which is operational within the ICON (Icosahedral Nonhydrostatic) numerical weather prediction model used operationally by DWD. For aviation purposes, the procedure provides the cubic root of the eddy dissipation rate ε 1/3 as an overall turbulence index. This quantity is a widely used measure for turbulence intensity as experienced by aircraft. The scheme includes additional sources of turbulent kinetic energy with particular relevance to aviation which are briefly introduced. These sources describe turbulence generation by the subgrid-scale action of wake eddies, mountain waves, convection as well as horizontal shear as found close to fronts or the jet stream. Furthermore, we introduce a post-processing, calibration to an empirical EDR distribution, and we demonstrate the potential as well as limitations of the final EDP-based turbulence forecast by considering several case studies of typical turbulence events. Finally, we reveal the forecasting capability of this product by verifying the model results against one year of aircraft in situ EDR measurements from commercial aircraft. We find that the forecasted EDP performs favorably when compared to the Ellrod index. In particular, the turbulence signal from deep convection, which is accounted for in the EDP product, is advantageous when spatial non-locality is allowed in the verification procedure.

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Chao Zhang, XiaoJing Jia, and Zhiping Wen

Abstract

This study investigated the increased impact of the spring (March-April-May) snow cover extent (SCE) over the western Tibetan Plateau (TP) (SSTP) to the Meiyu rainfall (June-July, JJ) over the Yangtze River valley (YRV) (MRYRV) after 1990s. The correlation between the MRYRV and SSTP is significantly increased from 1970-1992 (P1) to 1993-2015 (P2). In P1, the MRYRV-related SSTP anomalies locate southwest TP which cause a perturbation near the SWJ core and favor an eastward propagation in the form of a wave train. The wave train results in a southward shift of the SWJ over the ocean south of Japan in JJ and exerts a limited effect on the MRYRV. Differently, in P2, the MRYRV-related anomalous SSTP causes an anomalous cooling temperature and upper-level cyclonic system centered over the northwestern TP. The cyclonic system develops and extends eastward to the downstream region with time and reaches coastal East Asia in JJ. The anomalous westerly winds along its south flank cause an enhanced subtropical westerly jet (SWJ) which is accompanied by an anomalous lower-level air convergence and ascent motion near the YRV region, favoring enhanced MRYRV. In addition, the forecast experiments performed with empirical regression models illustrate that the prediction skill of the MRYRV variation is clearly increased in P2 with the additional forecast factor of the SSTP.

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