Evaluation of convection-permitting model simulations of cloud populations associated with the Madden-Julian Oscillation using data collected during the AMIE/DYNAMO field campaign

Abstract. Advanced satellite sensors are tasked with improving global-scale measurements of the Earth's atmosphere, clouds, and surface to enable enhancements in weather prediction, climate monitoring, and environmental change detection. Measurement system validation is crucial to achieving this goal and maximizing research and operational utility of resultant data. Field campaigns employing satellite under-flights with well-calibrated Fourier Transform Spectrometer (FTS) sensors aboard high-altitude aircraft are an essential part of this validation task. The National Polar-orbiting Operational Environmental Satellite System (NPOESS) Airborne Sounder Testbed-Interferometer (NAST-I) has been a fundamental contributor in this area by providing coincident high spectral and spatial resolution observations of infrared spectral radiances along with independently-retrieved geophysical products for comparison with like products from satellite sensors being validated. This manuscript focuses on validating infrared spectral radiance from the Infrared Atmospheric Sounding Interferometer (IASI) through a case study analysis using data obtained during the recent Joint Airborne IASI Validation Experiment (JAIVEx) field campaign. Emphasis is placed upon the benefits achievable from employing airborne interferometers such as the NAST-I since, in addition to IASI radiance calibration performance assessments, cross-validation with other advanced sounders such as the AQUA Atmospheric InfraRed Sounder (AIRS) is enabled.

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Impact of the Madden–Julian Oscillation on the Indonesian Throughflow in the Makassar Strait during the CINDY/DYNAMO Field Campaign

Journal of Climate ◽

10.1175/jcli-d-15-0711.1 ◽

2016 ◽

Vol 29 (17) ◽

pp. 6085-6108 ◽

Cited By ~ 6

Author(s):

Toshiaki Shinoda ◽

Weiqing Han ◽

Tommy G. Jensen ◽

Luis Zamudio ◽

E. Joseph Metzger ◽

...

Keyword(s):

Indian Ocean ◽

General Circulation ◽

Circulation Model ◽

Global Ocean ◽

Indonesian Throughflow ◽

Madden Julian Oscillation ◽

Field Campaign ◽

Eastern Boundary ◽

The Indian Ocean ◽

Velocity Anomalies

Abstract Previous studies indicate that equatorial zonal winds in the Indian Ocean can significantly influence the Indonesian Throughflow (ITF). During the Cooperative Indian Ocean Experiment on Intraseasonal Variability (CINDY)/Dynamics of the Madden–Julian Oscillation (DYNAMO) field campaign, two strong MJO events were observed within a month without a clear suppressed phase between them, and these events generated exceptionally strong ocean responses. Strong eastward currents along the equator in the Indian Ocean lasted more than one month from late November 2011 to early January 2012. The influence of these unique MJO events during the field campaign on ITF variability is investigated using a high-resolution (1/25°) global ocean general circulation model, the Hybrid Coordinate Ocean Model (HYCOM). The strong westerlies associated with these MJO events, which exceed 10 m s−1, generate strong equatorial eastward jets and downwelling near the eastern boundary. The equatorial jets are realistically simulated by the global HYCOM based on the comparison with the data collected during the field campaign. The analysis demonstrates that sea surface height (SSH) and alongshore velocity anomalies at the eastern boundary propagate along the coast of Sumatra and Java as coastal Kelvin waves, significantly reducing the ITF transport at the Makassar Strait during January–early February. The alongshore velocity anomalies associated with the Kelvin wave significantly leads SSH anomalies. The magnitude of the anomalous currents at the Makassar Strait is exceptionally large because of the unique feature of the MJO events, and thus the typical seasonal cycle of ITF could be significantly altered by strong MJO events such as those observed during the CINDY/DYNAMO field campaign.

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An oceanic pathway for Madden–Julian Oscillation influence on Maritime Continent Tropical Cyclones

npj Climate and Atmospheric Science ◽

10.1038/s41612-021-00208-4 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Karthik Balaguru ◽

L. Ruby Leung ◽

Samson M. Hagos ◽

Sujith Krishnakumar

Keyword(s):

Numerical Model ◽

Tropical Cyclones ◽

Large Scale ◽

Sea Surface ◽

Madden Julian Oscillation ◽

Surface Cooling ◽

Maritime Continent ◽

Large Scale Circulation ◽

Model Simulations ◽

Sea Surface Cooling

AbstractWhile the Madden–Julian Oscillation (MJO) has been shown to affect tropical cyclones (TCs) worldwide through its modulation of large-scale circulation in the atmosphere, little or no role for the ocean has been identified to date in this influence of MJO on TCs. Using observations and numerical model simulations, we demonstrate that MJO events substantially impact TCs over the Maritime Continent (MC) region through an oceanic pathway. While propagating across the MC region, MJO events cause significant sea surface cooling with an area-averaged value of about 0.35 ± 0.12 °C. Hence, TCs over the MC region immediately following the passage of MJO events encounter considerably cooler sea surface temperatures. Consequently, the enthalpy fluxes under the storms are reduced and the intensification rates decrease by more than 50% on average. These results highlight an important role played by the ocean in facilitating MJO-induced sub-seasonal variability in TC activity over the MC region.

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Climate change increases the probability of heavy rains like those of storm Desmond in the UK – an event attribution study in near-real time

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-13197-2015 ◽

2015 ◽

Vol 12 (12) ◽

pp. 13197-13216 ◽

Cited By ~ 21

Author(s):

G. J. van Oldenborgh ◽

F. E. L. Otto ◽

K. Haustein ◽

H. Cullen

Keyword(s):

Climate Change ◽

Climate Model ◽

Extreme Event ◽

Initial Assessment ◽

Model Simulations ◽

Event Attribution ◽

Climate Model Simulations ◽

Using Data ◽

The Uk ◽

Precipitation Events

Abstract. On 4–6 December 2015, the storm "Desmond" caused very heavy rainfall in northern England and Scotland, which led to widespread flooding. Here we provide an initial assessment of the influence of anthropogenic climate change on the likelihood of one-day precipitation events averaged over an area encompassing northern England and southern Scotland using data and methods available immediately after the event occurred. The analysis is based on three independent methods of extreme event attribution: historical observed trends, coupled climate model simulations and a large ensemble of regional model simulations. All three methods agree that the effect of climate change is positive, making precipitation events like this about 40 % more likely, with a provisional 2.5–97.5 % confidence interval of 5–80 %.

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A hydrologic post-processor for ensemble streamflow predictions

Advances in Geosciences ◽

10.5194/adgeo-29-51-2011 ◽

2011 ◽

Vol 29 ◽

pp. 51-59 ◽

Cited By ~ 40

Author(s):

L. Zhao ◽

Q. Duan ◽

J. Schaake ◽

A. Ye ◽

J. Xia

Keyword(s):

Probability Distributions ◽

Hydrologic Model ◽

Model Simulations ◽

Daily Streamflow ◽

Post Process ◽

Process Simulations ◽

Analysis Window ◽

Streamflow Data ◽

Calibration And Verification ◽

Using Data

Abstract. This paper evaluates the performance of a statistical post-processor for imperfect hydrologic model forecasts. Assuming that the meteorological forecasts are well-calibrated, we employ a "General Linear Model (GLM)" to post-process simulations produced by a hydrologic model. For a particular forecast date, the observations and simulations from an "analysis window" and hydrologic model forecasts for a "forecast window", the GLM Post-Processor (GLMPP) is used to produce an ensemble of predictions of the streamflow observations that will occur during the "forecast window". The objectives of the GLMPP are to: (1) preserve any skill in the original hydrologic ensemble forecast; (2) correct systematic model biases; (3) retain the equal-likelihood assumption for the ensemble; (4) preserve temporal scale dependency relationships in streamflow hydrographs and the uncertainty in the predictions; and, (5) produce reliable ensemble predictions. Observed and simulated daily streamflow data from the Second Workshop on Model Parameter Estimation Experiment (MOPEX) are used to test how well these objectives are met when the GLMPP is applied to ensemble hydrologic forecasts driven by well calibrated meteorological forecasts. A 39-year hydrologic dataset from the French Broad basin is split into calibration and verification periods. The results show that the GLMPP built using data from the calibration period removes the mean bias when applied to hydrologic model simulations from both the calibration and verification periods. Probability distributions of the post-processed model simulations are shown to be closer to the climatological probability distributions of observed streamflow than the distributions of the unadjusted simulated flows. A number of experiments with different GLMPP configurations were also conducted to examine the effects of different configurations for forecast and analysis window lengths on the robustness of the results.

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Assessing the response of forest productivity to climate extremes in Switzerland using model-data fusion

10.5194/egusphere-egu2020-16543 ◽

2020 ◽

Author(s):

Volodymyr Trotsiuk ◽

Keyword(s):

Data Assimilation ◽

Forest Ecosystem ◽

Climate Extremes ◽

Ecosystem Model ◽

Elevational Gradient ◽

Forest Productivity ◽

Carbon Stocks ◽

Forest Monitoring ◽

Model Simulations ◽

Using Data

Under unprecedent climate change and increased frequency of extreme events, e.g. drought, it is important to assess and forecast forest ecosystem vulnerability and stability. Large volumes of data from observational and experimental networks, increases in computational power, advances in ecological models, and optimization methodologies are the main measures to improve quantitative forecasting in ecology. Data assimilation is a key tool to improve ecosystem state prediction and forecasting by combining model simulations and observations. We assimilated observations of carbon stocks and fluxes from 271 permanent long-term forest monitoring plots across Switzerland into the 3-PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from 14% to 5% for forest stem carbon stocks and from 45% to 9% for stem carbon stock changes, respectively. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960-2018 and tested for climate-induced shifts in productivity along elevational gradient and in extreme years. Overall, we demonstrated a high potential of using data assimilation to improve predictions of forest ecosystem productivity. Furthermore, our calibrated model simulations suggest that climate extremes affect forest productivity in more complex ways than by simply shifting the response upwards in elevation.

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