Variability In Model Simulations Research Articles

An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system

Abstract. The accurate parameterization of atmospheric surface layer processes is crucial for weather forecasts using numerical weather prediction models. Here, an attempt has been made to improve the surface layer parameterization in the Weather Research and Forecasting model version 4.2.2 (WRFv4.2.2) by implementing similarity functions proposed by Kader and Yaglom (1990) to make it consistent in producing the transfer coefficient for momentum observed over the tropical region (Srivastava and Sharan, 2015). The surface layer module in WRFv4.2.2 is modified in such a way that it contains the commonly used similarity functions for momentum (φm) and heat (φh) under convective conditions instead of the existing single functional form. The updated module has various alternatives of φm and φh, which can be controlled by a flag introduced in the input file. The impacts of utilizing different functional forms have been evaluated using the bulk flux algorithm as well as real-case simulations with the WRFv4.2.2 model. The model-simulated variables have been evaluated with observational data from a flux tower at Ranchi (23.412° N, 85.440° E), India, and the ERA5-Land reanalysis dataset. The transfer coefficient for momentum simulated using the implemented scheme is found to agree well with its observed non-monotonic behavior in convective conditions (Srivastava and Sharan, 2021). The study suggests that the updated surface layer scheme performs well in simulating the surface transfer coefficients and could be potentially utilized for the parameterization of surface fluxes under convective conditions in the WRF model.

An observing system experiment framework for the tropical Indian Ocean salinity: A case study using a constellation of three satellites

In this study impact of assimilating Sea Surface Salinity (SSS) from multi-satellites (SMOS, Aquarius and SMAP) on numerical ocean model simulations in the north Indian Ocean has been analysed under the observing system experiment (OSE) framework. Daily data sets of Aquarius, SMAP and SMOS, which were available for a common period of April–May 2015, are used to constrain the ocean model using ensemble optimal interpolation technique. Apart from the control simulation in which satellite data were not assimilated, a total of seven assimilation experiments using different combinations of satellite SSS were conducted. The impact of assimilation experiments is analysed by comparing the model-simulated variables with in situ observations. Assimilating satellite SSS results in a reduction in Root Mean Square Error (RMSE) in SSS (∼ 54%) and also in subsurface salinity (∼ 21%) over the control run. The impact of assimilating SMAP observations is maximum on model simulations with the errors reducing by ∼ 54%. Subsurface salinity improvement is better with three satellites with ∼31% improvement in RMSE in the halocline region, which was ∼11% more than single satellite assimilation. Assimilation of SSS also resulted in improved simulations of the model surface, subsurface temperature and mixed layer depth. Model results show the ability of SSS observations to complement other ocean observation networks. One important observation from this study is that while the impact of assimilating SSS observations from a single satellite was on par with the impact of assimilating SSS observations from two or three satellites in correcting simulated surface salinity, assimilation from more than one satellite had a larger impact in the salinity of deeper layers of the ocean.

A Weibull Approach for Improving Climate Model Projections of Tropical Cyclone Wind-Speed Distributions

AbstractReliable estimates of future changes in extreme weather phenomena, such as tropical cyclone maximum wind speeds, are critical for climate change impact assessments and the development of appropriate adaptation strategies. However, global and regional climate model outputs are often too coarse for direct use in these applications, with variables such as wind speed having truncated probability distributions compared to those of observations. This poses two problems: How can model-simulated variables best be adjusted to make them more realistic? And how can such adjustments be used to make more reliable predictions of future changes in their distribution?This study investigates North Atlantic tropical cyclone maximum wind speeds from observations (1950–2010) and regional climate model simulations (1995–2005 and 2045–55 at 12- and 36-km spatial resolutions). The wind speed distributions in these datasets are well represented by the Weibull distribution, albeit with different scale and shape parameters.A power-law transfer function is used to recalibrate the Weibull variables and obtain future projections of wind speeds. Two different strategies, bias correction and change factor, are tested by using 36-km model data to predict future 12-km model data (pseudo-observations). The strategies are also applied to the observations to obtain likely predictions of the future distributions of wind speeds. The strategies yield similar predictions of likely changes in the fraction of events within Saffir–Simpson categories—for example, an increase from 21% (1995–2005) to 27%–37% (2045–55) for category 3 or above events and an increase from 1.6% (1995–2005) to 2.8%–9.8% (2045–55) for category 5 events.

Developing spatially stratified N 2O emission factors for Europe

We investigate the possibility to replace the – so-called – Tier 1 IPCC approach to estimate soil N 2O emissions with stratified emissions factors that take into account both N-input and the spatial variability of the environmental conditions within the countries of the European Union, using the DNDC-Europe model. Spatial variability in model simulations is high and corresponds to the variability reported in literature for field data. Our results indicate that (a) much of the observed variability in N 2O fluxes reflects the response of soils to external conditions, (b) it is likely that national inventories tend to overestimate the uncertainties in their estimated direct N 2O emissions from arable soils; (c) on average over Europe, the fertilizer-induced emissions (FIE) coincide with the IPCC factors, but they display large spatial variations. Therefore, at scales of individual countries or smaller, a stratified approach considering fertilizer type, soil characteristics and climatic parameters is preferable.

Mesoscale spectrum of atmospheric motions investigated in a very fine resolution global general circulation model

The horizontal spectrum of wind variance, conventionally referred to as the kinetic energy spectrum, is examined in experiments conducted with the Atmospheric GCM for the Earth Simulator (AFES) global spectral general circulation model. We find that the control version of AFES run at T639 horizontal spectral resolution simulates a kinetic energy spectrum that compares well at large scales with global observational reanalyses and, at smaller scales, with available aircraft observations at near‐tropopause levels. Specifically there is a roughly −3 power‐law dependence on horizontal wave number for wavelengths between about 5000 and 500 km, transitioning to a shallower mesoscale regime at smaller wavelengths. This is seen for both one‐dimensional spectra and for the two‐dimensional total wave number spectrum based on a spherical harmonic analysis. The simulated spectrum at midtropospheric levels is similar in that there is a transition to a shallower mesoscale regime, but the spectrum in the mesoscale is clearly steeper at midtroposphere than near the tropopause. There seem to be no extensive observations of horizontal spectra available in the midtroposphere, so it is not known whether the contrast seen in the model between upper and mid tropospheric levels is realistic. The dependence of the model simulated variability on the subgrid‐scale moist convection parameterization is examined. The space‐time variability of rainfall is shown to depend strongly on the convection scheme employed. The tropospheric kinetic energy spectrum in the mesoscale seems to be correlated with the precipitation behavior, so that in a version with a more variable precipitation field the kinetic energy in the mesoscale is enhanced. This suggests that the mesoscale motions in the model may be directly forced to a significant extent by the variability in the latent heating field. Experiments were also performed with a dry dynamical core version of the model run at both T639 and T1279 resolutions. This version also simulated a shallow mesoscale range, supporting the view that the mesoscale regime in the atmosphere is energized, at least in part, by a predominantly forward (i.e., downscale) nonlinear spectral cascade. Experiments with various formulations of the hyperdiffusion horizontal mixing parameterization show that the kinetic energy spectrum over about the last half of the resolved wave number range is under strong control by the parameterized mixing. However, the T1279 model simulates almost a decade of the shallow mesoscale regime (i.e., for horizontal wavelengths from about 80 to 500 km) that appears to be fairly independent of the diffusion employed. Finally, experiments are conducted in the dry version to see the effects on the kinetic energy spectrum of changing the thermal Rossby number for the simulations.

Impact of Satellite-Derived Forcings on Numerical Ocean Model Simulations and Study of Sea Surface Salinity Variations in the Indian Ocean

AbstractThis study focuses on two major aspects: the impact of satellite forcings (winds and precipitation) on the simulations of a multilayer Indian Ocean (IO) model (IOM) and the analysis of the processes responsible for salinity variations in the Indian Ocean during dipole years (1994 and 1997). It is observed that the European Remote Sensing Satellite-2 (ERS-2) scatterometer wind-driven solutions describe the interannual variabilities of sea surface temperature (SST) more realistically than the National Centers for Environmental Prediction (NCEP) wind-driven solutions. The equatorial westward current jet [hereafter referred to as reverse Wyrtki jet (RWJ)] originating near the Sumatra coast in response to anomalous easterlies during fall of 1994 and 1997 is quite strong in the scatterometer-forced solutions. This RWJ is found to be weak in the NCEP solution. Two more experiments differing by their precipitation forcings [climatological and interannually varying Global Precipitation Climatology Project (GPCP) rainfall] are carried out. Model-simulated variables like SST, sea surface salinity (SSS), and mixed layer depth (MLD) have been compared with in situ observations to verify the performance of the model. The model suggests a dipolelike structure in surface salinity during late 1994 and 1997, with low salinity in the central equatorial Indian Ocean (EIO) and high salinity near the Sumatra coast. The low-salinity tongue is caused by the transport of fresh surface waters via RWJ, which is further strengthened by a southward branch (which is absent in normal years) coming from the Bay of Bengal. A major inference of the study is that the low-salinity tongue is caused mainly by advection, not by a direct effect of precipitation. On the contrary, the high salinity near the Sumatra coast is due to the strong upwelling caused by anomalous easterlies. Another inference made out of this study is that there is apparently a definite signature of the evolution of the dipole event in the MLD approximately 2 months prior to the peak occurring in SSS in the south EIO.