scholarly journals Role of Maritime Continent Land Convection on the Mean State and MJO Propagation

2020 ◽  
Vol 33 (5) ◽  
pp. 1659-1675 ◽  
Author(s):  
Min-Seop Ahn ◽  
Daehyun Kim ◽  
Yoo-Geun Ham ◽  
Sungsu Park
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Critical Role ◽  
Moisture Gradient ◽  
Moisture Distribution ◽  
Boreal Winter ◽  
Maritime Continent ◽  
The Mean

AbstractThe Maritime Continent (MC) region is known as a “barrier” in the life cycle of the Madden–Julian oscillation (MJO). During boreal winter, the MJO detours the equatorial MC land region southward and propagates through the oceanic region. Also, about half of the MJO events that initiate over the Indian Ocean cease around the MC. The mechanism through which the MC affects MJO propagation, however, has remained unanswered. The current study investigates the MJO–MC interaction with a particular focus on the role of MC land convection. Using a global climate model that simulates both mean climate and MJO realistically, we performed two sensitivity experiments in which updraft plume radius is set to its maximum and minimum value only in the MC land grid points, making convective top deeper and shallower, respectively. Our results show that MC land convection plays a key role in shaping the 3D climatological moisture distribution around the MC through its local and nonlocal effects. Shallower and weaker MC land convection results in a steepening of the vertical and meridional mean moisture gradient over the MC region. The opposite is the case when MC land convection becomes deeper and stronger. The MJO’s eastward propagation is enhanced (suppressed) with the steeper (lower) mean moisture gradient. The moist static energy (MSE) budget of the MJO reveals the vertical and meridional advection of the mean MSE by MJO wind anomalies as the key processes that are responsible for the changes in MJO propagation characteristics. Our results pinpoint the critical role of the background moisture gradient on MJO propagation.

scholarly journals The Role of Background Meridional Moisture Gradient on the Propagation of the MJO over the Maritime Continent

2021 ◽  
pp. 1-54
Author(s):  
Daehyun Kang ◽  
Daehyun Kim ◽  
Min-Seop Ahn ◽  
Soon-Il An
Keyword(s):  
Southern Oscillation ◽  
Moisture Gradient ◽  
Moisture Distribution ◽  
Maritime Continent ◽  
Budget Analysis ◽  
Sea Surface Temperature Anomalies ◽  
The Mean ◽  
Strong Control ◽  
Meridional Advection

AbstractThis study investigates the role of the background meridional moisture gradient (MMG) on the propagation of the Madden–Julian Oscillation (MJO) across the Maritime Continent (MC) region. It is found that the interannual variability of the seasonal mean MMG over the southern MC area is associated with the meridional expansion and contraction of the moist area in the vicinity of the MC. Sea surface temperature anomalies associated with relatively high and low seasonal mean MMG exhibit patterns that resemble those of the El Niño–Southern Oscillation. By contrasting the years with anomalously low and high MMG, we show that MJO propagation through the MC is enhanced (suppressed) in years with higher (lower) seasonal mean MMG, though the effect is less robust when MMG anomalies are weak. Column-integrated moisture budget analysis further shows that sufficiently large MMG anomalies affects MJO activity by modulating the meridional advection of the mean moisture via MJO wind anomalies. Our results suggest that the background moisture distribution has a strong control over the propagation characteristics of the MJO in the MC region.

The MJO-QBO Relationship in a GCM with Stratospheric Nudging

2021 ◽  
pp. 1-69
Author(s):  
Zane Martin ◽  
Clara Orbe ◽  
Shuguang Wang ◽  
Adam Sobel
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Boreal Winter ◽  
Quasi Biennial Oscillation ◽  
Meridional Winds ◽  
Goddard Institute ◽  
Minimal Bias

AbstractObservational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection.

scholarly journals The role of serial European windstorm clustering for extreme seasonal losses as determined from multi-centennial simulations of high-resolution global climate model data

2018 ◽  
Vol 18 (11) ◽  
pp. 2991-3006 ◽  
Author(s):  
Matthew D. K. Priestley ◽  
Helen F. Dacre ◽  
Len C. Shaffrey ◽  
Kevin I. Hodges ◽  
Joaquim G. Pinto
Keyword(s):  
High Resolution ◽  
Return Period ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Geographic Region ◽  
Near Surface ◽  
Short Period

Abstract. Extratropical cyclones are the most damaging natural hazard to affect western Europe. Serial clustering occurs when many intense cyclones affect one specific geographic region in a short period of time which can potentially lead to very large seasonal losses. Previous studies have shown that intense cyclones may be more likely to cluster than less intense cyclones. We revisit this topic using a high-resolution climate model with the aim to determine how important clustering is for windstorm-related losses. The role of windstorm clustering is investigated using a quantifiable metric (storm severity index, SSI) that is based on near-surface meteorological variables (10 m wind speed) and is a good proxy for losses. The SSI is used to convert a wind footprint into losses for individual windstorms or seasons. 918 years of a present-day ensemble of coupled climate model simulations from the High-Resolution Global Environment Model (HiGEM) are compared to ERA-Interim reanalysis. HiGEM is able to successfully reproduce the wintertime North Atlantic/European circulation, and represent the large-scale circulation associated with the serial clustering of European windstorms. We use two measures to identify any changes in the contribution of clustering to the seasonal windstorm loss as a function of return period. Above a return period of 3 years, the accumulated seasonal loss from HiGEM is up to 20 % larger than the accumulated seasonal loss from a set of random resamples of the HiGEM data. Seasonal losses are increased by 10 %–20 % relative to randomized seasonal losses at a return period of 200 years. The contribution of the single largest event in a season to the accumulated seasonal loss does not change with return period, generally ranging between 25 % and 50 %. Given the realistic dynamical representation of cyclone clustering in HiGEM, and comparable statistics to ERA-Interim, we conclude that our estimation of clustering and its dependence on the return period will be useful for informing the development of risk models for European windstorms, particularly for longer return periods.

scholarly journals A Source–Receptor Perspective on the Polar Hydrologic Cycle: Sources, Seasonality, and Arctic–Antarctic Parity in the Hydrologic Cycle Response to CO2 Doubling

2017 ◽  
Vol 30 (24) ◽  
pp. 9999-10017 ◽  
Author(s):  
Hansi K. A. Singh ◽  
Cecilia M. Bitz ◽  
Aaron Donohoe ◽  
Philip J. Rasch
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Critical Role ◽  
Winter Season ◽  
Hydrologic Cycle ◽  
The Arctic ◽  
Moisture Sources ◽  
Proxy Records ◽  
Transport Length

Numerical water tracers implemented in a global climate model are used to study how polar hydroclimate responds to CO2-induced warming from a source–receptor perspective. Although remote moisture sources contribute substantially more to polar precipitation year-round in the mean state, an increase in locally sourced moisture is crucial to the winter season polar precipitation response to greenhouse gas forcing. In general, the polar hydroclimate response to CO2-induced warming is strongly seasonal: over both the Arctic and Antarctic, locally sourced moisture constitutes a larger fraction of the precipitation in winter, while remote sources become even more dominant in summer. Increased local evaporation in fall and winter is coincident with sea ice retreat, which greatly augments local moisture sources in these seasons. In summer, however, larger contributions from more remote moisture source regions are consistent with an increase in moisture residence times and a longer moisture transport length scale, which produces a robust hydrologic cycle response to CO2-induced warming globally. The critical role of locally sourced moisture in the hydrologic cycle response of both the Arctic and Antarctic is distinct from controlling factors elsewhere on the globe; for this reason, great care should be taken in interpreting polar isotopic proxy records from climate states unlike the present.

Parameterizing gravity waves in the DYNAMICO-Saturn Global Climate Model to understand Saturn's equatorial oscillation

2020 ◽  
Author(s):  
Deborah Bardet ◽  
Aymeric Spiga ◽  
Sandrine Guerlet ◽  
Ehouarn Millour ◽  
François Lott
Keyword(s):  
Gravity Waves ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Life Cycles ◽  
Small Scale ◽  
Mean Flow ◽  
Time Step ◽  
Maximum Value ◽  
The Mean

<p>To address questions about the driving mechanisms of Saturn's equatorial oscillation, our team at the Laboratoire de Météorologie Dynamique built the DYNAMICO-Saturn Global Climate Model to study tropospheric dynamics, tropospheric waves activity (Spiga et al. 2020) and equatorial stratospheric dynamics (Bardet et al. 2020) of Saturn. Previous studies (Guerlet et al. 2014, Spiga et al. 2020, Cabanes et al. 2020) have shown that our model produces consistent thermal structure and seasonal variability compared to Cassini CIRS measurements, mid-latitude eddy-driven tropospheric eastward and westward jets commensurate to those observed and following the zonostrophic regime, and planetary-scale waves such as Rossby-gravity (Yanai), Rossby and Kelvin waves in the tropical channel. Extending the model top toward the upper stratosphere allowed our model to produce an almost semi-annual equatorial oscillation with opposite eastward and westward phases. Associated temperature anomalies have a similar behavior than the Cassini/CIRS observations, but the amplitude of the temperature oscillation is twice smaller than the observed one. The absence of sub-grid-scale waves in the model produces an imbalance in eastward- and westward-wave forcing on the mean flow and could be an explanation to the irregularity in both the oscillating period and the downward rate propagation of the resolved Saturn equatorial oscillation.</p> <p>To explore the impact of those small-scale waves on the spontaneous equatorial oscillation emerging in the DYNAMICO-Saturn GCM (Bardet et al. 2020), we add a sub-grid-scale non-orographic gravity waves drag parameterization in our model.<br />This parameterization is directly adapted from the stochastic terrestrial model of Lott et al. (2012). This formalism represents a broadband gravity wave spectrum, using the superposition of a large statistical set of monochromatic waves. As the time scale of the life cycles of gravity waves is much longer than the time step of our GCM, our parametrization can launch a few waves whose characteristics are randomly chosen at each time step. This stochastic gravity waves drag parameterization is applied in DYNAMICO-Saturn on all points of the horizontal grid.</p> <p>A key parameter used in the non-orographic gravity waves drag parameterization is the maximum value of the Eliassen-Palm flux. The Eliassen Palm flux represents the momentum carried by waves that could be transferred to the mean flow. This value has never been measured in Saturn's atmosphere and it represents an important degree of freedom in the parameterization of gravity waves.</p> <p>We performed several test simulations, lasting two Saturn years whose initial state is derived from Bardet et al (2020), with an horizontal resolution of 1/2° in longitude/latitude and a vertical resolution ranging between 3 bar to 1 μbar. For these test simulations, the maximum value of the Eliassen-Palm fulx is set to 10<sup>-6</sup>, 10<sup>-5</sup>, 10<sup>-4</sup> and 10<sup>-3</sup> kg m<sup>-1</sup> s<sup>-2</sup>. </p> <p>Preliminary results show that the appropriate value of our main parameter is between 10<sup>-5</sup> and 10<sup>-4</sup> kg m<sup>-1</sup> s<sup>-2</sup>. Eliassen-Palm flux value of 10<sup>-3</sup> kg m<sup>-1</sup> s<sup>-2</sup> demonstrates a too large impact: the equatorial oscillation is entirely vanished is this configuration. The simulation using the value of 10<sup>-6</sup> kg m<sup>-1</sup> s<sup>-2</sup> is equivalent to the control simulation without the gravity waves drag parameterization.  </p> <p>The next step is to test other parameters, as phase velocity of the gravity waves, horizontal wavenumber, to understand how gravity waves impact the equatorial oscillation.</p>

Classical Statistics

2013 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Random Variable ◽  
Data File ◽  
The United States ◽  
Descriptive Statistics ◽  
Sample Mean ◽  
Data Object ◽  
The Mean

All hurricanes are different. Statistics helps you characterize hurricanes from the typical to the extreme. In this chapter, we provide an introduction to classical (or frequentist) statistics. To get the most out of it, we again encourage you to open an R session and type in the code as you read along. Descriptive statistics are used to summarize your data. The mean and the variance are good examples. So is correlation. Data can be a set of weather records or output from a global climate model. Descriptive statistics provide answers to questions like does Jamaica experience more hurricanes than Puerto Rico? In Chapter 2, you learned some functions for summarizing your data, let us review. Recall that the data set H.txt is a list of hurricane counts by year making landfall in the United States (excluding Hawaii). To input the data and save them as a data object, type . . . > H = read. Table ("H.txt", header=TRUE) . . . Make sure the data file is located in your working directory. To check your working directory, type getwd (). Sometimes all you need are a few summary statistics from your data. You can obtain the mean and variance by typing . . . > mean (H$All); var (H$All) [1] 1.69375 [1] 2.10059 . . . Recall that the semicolon acts as a return so you can place multiple functions on the same text line. The sample mean is a measure of the central tendency and the sample variance is a measure of the spread. These are called the first-and second-moment statistics. Like all statistics, they are random variables. A random variable can be thought of as a quantity whose value is not fixed; it changes depending on the values in your sample. If you consider the number of hurricanes over a different sample of years, the sample mean will almost certainly be different. Same with the variance. The sample mean provides an estimate of the population mean (the mean over all past and future years).

scholarly journals A Unified Moisture Mode Framework for Seasonality of the Madden–Julian Oscillation

2018 ◽  
Vol 31 (11) ◽  
pp. 4215-4224 ◽  
Author(s):  
Xianan Jiang ◽  
Ángel F. Adames ◽  
Ming Zhao ◽  
Duane Waliser ◽  
Eric Maloney
Keyword(s):  
Close Association ◽  
Critical Role ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Equatorial Wave ◽  
Moisture Mode ◽  
Northward Propagation ◽  
The Mean ◽  
Moisture Pattern

The Madden–Julian oscillation (MJO) exhibits pronounced seasonality. While it is largely characterized by equatorially eastward propagation during the boreal winter, MJO convection undergoes marked poleward movement over the Asian monsoon region during summer, producing a significant modulation of monsoon rainfall. In classical MJO theories that seek to interpret the distinct seasonality in MJO propagation features, the role of equatorial wave dynamics has been emphasized for its eastward propagation, whereas coupling between MJO convection and the mean monsoon flow is considered essential for its northward propagation. In this study, a unified physical framework based on the moisture mode theory, is offered to explain the seasonality in MJO propagation. Moistening and drying caused by horizontal advection of the lower-tropospheric mean moisture by MJO winds, which was recently found to be critical for the eastward propagation of the winter MJO, is also shown to play a dominant role in operating the northward propagation of the summer MJO. The seasonal variations in the mean moisture pattern largely shape the distinct MJO propagation in different seasons. The critical role of the seasonally varying climatological distribution of moisture for the MJO propagation is further supported by the close association between model skill in representing the MJO propagation and skill at producing the lower-tropospheric mean moisture pattern. This study thus pinpoints an important direction for climate model development for improved MJO representation during all seasons.

scholarly journals Climatic impacts on water resources in a tropical catchment in Uganda and adaptation measures proposed by resident stakeholders

2021 ◽  
Vol 164 (1-2) ◽  
Author(s):  
Bano Mehdi ◽  
Julie Dekens ◽  
Mathew Herrnegger
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Mean Annual Precipitation ◽  
Adaptation Measures ◽  
The Mean ◽  
The Impact ◽  
Far Future

AbstractThe Ruhezamyenda catchment in Uganda includes a unique lake, Lake Bunyonyi, and is threatened by increasing social and environmental pressures. The COSERO hydrological model was used to assess the impact of climate change on future surface runoff and evapotranspiration in the Lake Bunyonyi catchment (381 km2). The model was forced with an ensemble of CMIP5 global climate model (GCM) simulations for the mid-term future (2041–2070) and for the far future (2071–2100), each with RCP4.5 and RCP8.5. In the Ruhezamyenda catchment, compared to 1971–2000, the median of all GCMs (for both RCPs) showed the mean monthly air temperature to increase by approximately 1.5 to 3.0 °C in the mid-term future and by roughly 2.0 to 4.5 °C in the far future. The mean annual precipitation is generally projected to increase, with future changes between − 25 and + 75% (RCP8.5). AET in the Lake Bunyonyi catchment was simulated to increase for the future by approximately + 8 mm/month in the median of all GCMs for RCP8.5 for the far future. The runoff for future periods showed much uncertainty, but with an overall increasing trend. A combination of no-regrets adaptation options in the five categories of: governance; communication and capacity development; water, soil, land management and livelihoods improvement; data management; and research, was identified and validated with stakeholders, who also identified additional adaptation actions based on the model results. This study contributes to improving scientific knowledge on the impacts of climate change on water resources in Uganda with the purpose to support adaptation.

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